Search Results (151)

Vietnam’s domestic gold price has persistently exceeded the world price by a wide margin, even as recent reforms have begun to relax the state’s historical monopoly over gold-bar production and imports. This paper asks why the gap persists, and whether it is confined to normal market conditions or extends into periods of extreme price movement. Using daily data spanning 2 January 2019 to 31 July 2026 (1856 trading days), covering the reform introduced by Decree No. 232/2025/ND-CP we decompose the domestic premium into a currency component and a pure physical-gold component, and use a copula-based framework to separately assess average price linkage and tail (extreme-event) co-movement between the domestic and world markets. Domestic gold bars traded at an average premium of 16.0% over import-parity world prices, of which 13.8 percentage points reflect the physical-gold component driven by constrained arbitrage, while currency factors account for only about 2 percentage points. The average linkage between the two markets is weak, indicating persistent segmentation, and this segmentation extends into the tails of the distribution for most of the sample. The premium itself carries substantial latent risk: a reversion to price parity would imply a one-off loss of about 9.6% of value, roughly eight to ten times the historical one-day 5% Value-at-Risk. Following the reform’s effective date, however, we find early evidence of emerging co-movement specifically in extreme upside price movements, even though the physical premium itself has not yet narrowed—consistent with a reform that has been enacted in law but remains at an early stage of operational implementation. The results indicate that administrative restrictions on the physical gold supply chain, rather than currency controls, are the principal source of Vietnam’s persistent gold-price gap, with direct implications for how the ongoing liberalization process should be sequenced. Full article

Let $X={\left(X_t\right)}_{0\le t\le T}$ be a real-valued continuous process. For a threshold a, the sub-threshold time set $E_T\left(a\right)=\{t\in [0,T]:X_t\le a\}$ encodes several different threshold observables. The most elementary one is the cumulative occupation time $A_T\left(a\right)={\int }_0^T{\mathbf{1}}_{\{X_t\le a\}}dt.$ For a regular one-dimensional diffusion, the classical occupation density formula gives $A_T\left(a\right)={\int }_{-\infty }^a{\displaystyle \frac{L_T^y\left(X\right)}{{\sigma }^2\left(y\right)}}dy,$ and hence ${\displaystyle \frac{\partial A_T}{\partial a}}\left(a\right)={\displaystyle \frac{L_T^a\left(X\right)}{{\sigma }^2\left(a\right)}}.$ Thus additive threshold occupation admits a local-time sensitivity calculus. In the terminology of barrier contracts, this additive clock is the cumulative, non-resetting Parisian clock, also called the Parasian clock. The purpose of this paper is to contrast this additive/Parasian regime with the behavior of resetting Parisian burst functionals. The connected components of $E_T\left(a\right)$ represent sub-threshold episodes. We study in particular the longest burst $M_T\left(a\right)=sup\left\{\right|I|:I\mathrm{is}\mathrm{a}\mathrm{connected}\mathrm{component}\mathrm{of}{E}_T\left(a\right)\}.$ While $A_T$ is locally controlled by local time, $M_T$ is governed by the connectivity of the sub-threshold time set. We prove that $M_T$ is monotone, that its supremum is attained, and that the weak-sublevel version is right-continuous with left limits, while the strict-sublevel version is its left-continuous regularization. The jump at a level is the increase in the maximal connected-component length produced by adjoining the level set. This gives a deterministic càdlàg/càglàd calculus for longest-burst profiles. For regular one-dimensional diffusions, this yields a sharp structural contrast. At deterministic levels which are almost surely not local-extreme values, the weak and strict longest bursts agree almost surely. Whenever the path has a unique interior maximum, the level-indexed longest-burst profile has a positive jump at the maximum level and is therefore not absolutely continuous. Brownian motion satisfies this criterion almost surely. We further identify the deterministic mechanism behind this instability: small threshold increases may fill short temporal bridges and merge large sub-threshold components. Finally, we show that the longest burst is exactly a one-sided continuous Parisian functional. This yields an exact Laplace-transform representation of its Brownian law through the Chesney–Jeanblanc-Picqué–Yor Parisian transform, and an excursion-measure formulation in which local time enters only as the Itô excursion intensity. We also discuss smoothed burst statistics, moving thresholds, and diffusion examples. The paper is intended as a threshold-sensitivity comparison: local time controls cumulative Parasian occupation, whereas resetting Parisian burst observables are controlled by component mergers and excursion structure. Full article

The lunar South Pole has emerged as a strategic target for future space exploration due to its potential to host valuable resources and its favorable illumination conditions. However, the absence of a significant atmosphere leaves surface infrastructure directly exposed to meteoroid impacts, creating a critical challenge for long-term operations and mission sustainability. This study presents a probabilistic assessment of meteoroid impact hazard at the lunar South Polar region using version 3.0 of NASA’s Meteoroid Engineering Model (MEM 3.0). The proposed methodology integrates site-specific meteoroid flux estimation with probabilistic recurrence analysis to characterize meteoroid mass distributions, relative velocities, directional exposure conditions, and impact frequencies on the lunar surface. The results indicate that the meteoroid flux exhibits a well-defined peak near 25.5 km·s⁻¹, with maximum values approaching 0.053 impacts·m⁻² per year in the ram direction, confirming a pronounced directional anisotropy in the impact environment. Small particles dominate the cumulative impact frequency within the validated MEM 3.0 mass range (<10¹ g), whereas probabilistic extrapolation of the mass–frequency relationship suggests that extreme-mass impactors (~10⁶ g) occur far less frequently but generate substantially greater kinetic energies. Power law scaling relationships between impact frequency, particle mass, and return period are further used to define representative impact scenarios for engineering design and hazard assessment. The proposed methodology provides a physically consistent and scalable framework for evaluating meteoroid hazard conditions in localized lunar environments and supports the development of resilient infrastructure for future long-duration exploration missions at the lunar South Polar region. Full article

The green transition in agriculture is a key issue for achieving sustainable development. Based on panel data from 30 Chinese provinces covering the period from 2011 to 2022, this paper examines the relationship between environmental enforcement and agricultural green total factor productivity (AGTFP), with a focus on analyzing the moderating effects of land transfer and agricultural insurance, as well as their synergistic threshold characteristics. The study employs two-way fixed-effects models, moderating effect models, and Hansen threshold regression methods for empirical analysis. The baseline regression results show a significant positive association between environmental enforcement and AGTFP. This conclusion remains robust after various tests, including truncation, replacement of core explanatory variables, difference GMM, and instrumental variables. The decomposition test shows that this positive correlation is mainly reflected through the channel of technological progress, rather than the improvement in technical efficiency. Heterogeneity analysis indicates that the positive association is more pronounced in regions with high GDP, strong law enforcement capacity, and in northern regions. Moderation analysis reveals that both the land transfer rate and insurance depth positively moderate the relationship between environmental enforcement and AGTFP, and the two exhibit a synergistic effect. However, this synergistic effect exhibits nonlinear characteristics and may weaken or even reverse at extreme value intervals. A threshold model further reveals an asymmetric complementary relationship between the two institutional conditions. The moderating effect of land transfer is activated only after insurance depth crosses a threshold value, while the moderating effect of insurance depth is most effective during the small-scale farming stage. These findings suggest that environmental regulation policies should be advanced in coordination with land transfer and agricultural insurance systems, with a focus on institutional alignment and coordination. Full article

The proposed methodology combines analytical control laws with adaptive mechanisms and machine-learning-assisted modules based on regression trees, random forests, and extreme gradient boosting (XGBoost). Machine learning models are employed to approximate unknown nonlinear dynamics, compensate disturbances, and adjust controller parameters, while the overall control structure is constrained by Lyapunov stability conditions. This ensures that the inclusion of data-driven components does not violate the fundamental requirement of system stability. The effectiveness of the proposed approach is evaluated through simulation experiments across three operating modes with varying degrees of nonlinearity and dynamic complexity. The results show that hybrid models incorporating ensemble machine learning methods improved performance compared with the analytical and adaptive baselines examined. XGBoost-based control achieves the lowest error values and the highest level of Lyapunov stability compliance (up to 99.3%). The main contribution of this study lies in the development of a unified synthesis framework in which machine learning is not used as a standalone control strategy but as a machine-learning-assisted support mechanism integrated into a theoretically grounded control architecture. The proposed approach provides a balance between adaptability, accuracy, and rigorous stability guarantees, suggesting potential applicability to simulation-based and offline-assisted control design tasks, while real-time embedded implementation requires additional computational optimization and validation. Full article

To improve the path-following performance of an underactuated autonomous underwater vehicle (AUV) under varying path geometries and desired velocities, this study proposes a direct X-rudder control method based on Task-Informed Inductive-Bias Conservative Soft Actor–Critic (TIB-CSAC). The proposed method directly learns the X-rudder control policy from the path-following information of the current and subsequent path segments in a data-driven way, thereby avoiding the complex design and manual tuning of guidance laws and attitude controllers for rudder command generation. To support such two-segment policy learning, a task-informed inductive-bias encoder is proposed to construct structured and conditioned state representations, thereby improving sample efficiency and overall training quality. In addition, given the long-tail characteristics of task difficulty in agent training, a multi-head conservative value evaluation mechanism is incorporated to mitigate return drawdowns induced by challenging tasks in the tail stage of training and to enhance tail-stage convergence stability. The path-following performance is validated in three representative scenarios with different path pitch, path heading variations, and desired surge velocity conditions. The results show that, compared with the baseline soft actor–critic (SAC) method, TIB-CSAC improves multiple vertical and horizontal error metrics, including maximum absolute error, mean absolute error, tail error, and error threshold exceedance ratio. These results indicate that TIB-CSAC not only improves overall adherence to the reference path, but also more effectively suppresses extreme errors and tail errors, thereby demonstrating stronger path-following robustness and reliability. Full article

The turbine-driven coaxial boiler feed pump (TD-BFP) speed regulation system is a core auxiliary machine in thermal power generating units. Its complex physical characteristics, including strong square-law nonlinearity, multivariable coupling, and large inertia, pose significant challenges for conventional fixed-parameter PID controllers, which often suffer from severe regulation lag, integral windup, and high-frequency oscillation during wide-range operating condition transitions. To address these issues, an improved adaptive PID control strategy based on a Back Propagation (BP) neural network is proposed in this paper. Specifically, to overcome the negative control gradient loss caused by the square-law resistance in the physical model, a sign-preserving mapping logic ($u\mid u\mid$) is innovatively designed. Furthermore, a dynamic anti-integral windup mechanism with physical boundary constraints and a first-order inertial filtering algorithm is introduced. Comprehensive simulation experiments on the Matlab/Simulink platform under high-load step operating conditions (3683 r/min and 1104 t/h) reveal that the proposed algorithm achieves millisecond-level, zero-overshoot tracking. Quantitative evaluations demonstrate that, compared with the traditional PID controller, the proposed method reduces the Root Mean Square Error (RMSE) by 88.29% and the Integral of Absolute Error (IAE) by 93.75%, achieving a near-perfect goodness of fit ($R^2$) of 0.9998. Additionally, the Total Variation (TV) of the control command is substantially decreased. These results convincingly demonstrate that the proposed controller perfectly balances extremely high dynamic fitting accuracy with reduced mechanical wear, presenting exceptional engineering application value for the localization transformation of power plant control systems. Full article

We study a topological problem in discrete extreme value theory. Let $\mathcal{P}\left(\mathbb{N}\right)$ denote the space of probability laws on $\mathbb{N}$, endowed with the total variation metric $d_{\mathrm{TV}}$. Fix a rounding map r, either the ceiling map or the nearest-integer map. We consider the rounded discrete max-domain-of-attraction class ${\mathcal{D}}_r\subset \mathcal{P}\left(\mathbb{N}\right)$, consisting of all laws of the form $\mathrm{law}\left(r\right(Y\left)\right)$, where the parent law of Y belongs to a classical max domain of attraction $\mathrm{MDA}\left(G_{\xi }\right)$ for some generalized extreme value shape $\xi$. The main result of this note is that, for the ceiling and midpoint rounding schemes, each class ${\mathcal{D}}_r$ is meager in $\mathcal{P}\left(\mathbb{N}\right)$, while remaining dense; this mirrors the continuous picture of Leonetti and Khorrami Chokami. The proof is based on the Baire category theorem: we construct a comeager subset of $\mathcal{P}\left(\mathbb{N}\right)$ by forcing infinitely many incompatible dyadic tail ratios on disjoint far-out windows; such behavior cannot occur for any rounded law arising from a single classical max domain of attraction. We also record the corresponding Banach–Mazur game interpretation and explain why the argument applies to the rounded schemes (Types B and C) rather than to the sampled-density discretization (Type A). Full article

Hole cleaning ensures drilling safety and efficiency. Well inclination angle and drilling fluid density are important parameters affecting cuttings transport. To reveal their coupled interaction mechanism, this study employs the Euler–Euler multiphase flow model to conduct CFD simulations of cuttings transport in a 3D eccentric annulus with an eccentricity of 0.6 under various inclination angles (30°, 45°, 60°, 75°) and drilling fluid densities (1200~1800 kg/m³). Using the cuttings transport ratio (CTR), annulus cuttings volume concentration (CVT), outlet cuttings volume fraction, and annulus pressure drop as evaluation indicators, the influence mechanism of these parameters on hole cleaning efficiency is systematically analyzed. The results show that the effect of drilling fluid density on the CTR is regulated by inclination angle, with 45° being the critical angle for the extreme value of the CTR. Increasing density can significantly reduce cuttings deposition in the annulus, with a more pronounced improvement effect in high-inclination sections. Effective cuttings transport can be achieved by increasing the density to 1500, 1650, 1800, and 1800 kg/m³ for inclination angles of 30°, 45°, 60°, and 75°, respectively. The annulus pressure drop increases approximately linearly with density, and first rises then falls as the inclination angle increases from 30° to 75°, with 45° being the critical angle for peak pressure drop. This study clarifies the coupled regulation law of inclination angle and drilling fluid density, and determines the critical drilling fluid density under different inclinations, providing a numerical basis for optimizing hydraulic parameters and improving hole cleaning efficiency in directional drilling. Full article

Sloshing in partially filled tanks generates significant impact pressures that threaten the structural integrity of LNG cargo containment systems, and accurate scaling of these impacts remains a critical issue. Although Froude-based scaling has been widely applied, its validity may be limited under conditions where multiple impact mechanisms coexist. In this study, sloshing impact pressures measured across different scales were analyzed based on individual impact events. Distribution-based representative metrics, including mean and upper-percentile values, were introduced, and scale dependency was quantified using a power-law relationship. The results show that under low filling conditions, impact responses exhibit relatively consistent distributions, and gravity-based scaling yields nearly scale-independent results. In contrast, high filling conditions lead to increased variability and a pronounced expansion of the upper tail, resulting in stronger scale dependency, particularly for high-intensity events. The increase in the power-law exponent indicates that extreme impacts are more sensitive to scale variation. These findings demonstrate that sloshing impact scaling is governed not by a uniform change in pressure magnitude, but by a redistribution of impact intensity across events. Consequently, reliable scaling requires consideration of both distribution characteristics and underlying impact mechanisms. Full article

Magnetic flux leakage (MFL) testing is a critical non-destructive testing (NDT) method for ensuring the safety of ferromagnetic storage and transportation equipment. However, existing research has predominantly focused on weak or saturated magnetization states, leaving the characteristic laws and physical mechanisms of defect signals under supersaturated magnetization conditions unclear. To address this gap, this paper systematically investigates the MFL signal evolution mechanism and develops a high-precision quantitative identification method for defects under supersaturated magnetization conditions through finite element simulation, theoretical modeling, and experimental validation. First, a three-dimensional (3D) finite element model for MFL testing is established using COMSOL Multiphysics. The regulatory effects of key parameters—sensor lift-off value, defect burial depth, length, and depth—on the peak values and distribution characteristics of axial and radial MFL signals are revealed, a signal peak characterization model for each parameter and their adjusted $R^2$ is obtained via fitting, and the detection capability of the detector for defects with different shapes is simultaneously verified. Furthermore, actual detection is conducted on three crack defects of different sizes, and the analysis results indicate that the characterization models of each parameter obtained from the simulation exhibit high accuracy. The results show that MFL signal intensity under supersaturated magnetization conditions is significantly enhanced compared to that under saturated magnetization conditions. Furthermore, to improve defect length measurement accuracy, a signal correction method based on the midpoint of extreme values of the second derivative of axial signals is proposed. By compensating for peak offsets caused by factors like magnetic field diffusion, this method reduces the maximum defect length identification error from 14.25% (pre-correction) to below 0.3%. This study elucidates the coupling influence mechanism of multi-physical parameters on MFL signals under supersaturated magnetization conditions. The proposed high-precision signal correction method provides a novel theoretical basis and technical approach for the accurate quantification and inversion of defects in complex operating conditions. Full article

Global sediment reduction threatens deltaic sustainability and channel stability. While climatic and anthropogenic drivers are recognized, their cross-scale interactions remain poorly understood. This study investigated area-specific sediment yield (SSY) and its driving mechanisms across 14 stations (1.9 × 10⁴ to 1.0 × 10⁶ km²) in the Upper Yangtze River Basin (UYRB) from 1960 to 2018 using PLS-SEM and power-law scaling. Results show that by 2018, reservoir capacity reached 165.5 billion m³, regulating 38% of annual runoff. SSY significantly declined at 12 of 14 stations, with abrupt change points clustering around 1985. We found that intensive human interventions have fundamentally restructured the natural scale dependency of SSY, with the scaling exponent (β) shifting from a stable near-zero value to violent fluctuations (−0.2 to 0.5). Temporally, the dominant driver transitioned from hydro-climatic factors to dam-induced regulation. Spatially, the “filtering effect” of dams intensified with increasing drainage area, whereas smaller watersheds remained disproportionately sensitive to extreme precipitation. This scale-based divergence reveals a critical vulnerability: while mega-dams mitigate sediment at the basin scale, smaller catchments face elevated risks of high sediment delivery under intensifying climate extremes. These findings provide evidence of human-induced scaling instability in a large river system and highlight the necessity of scale-sensitive governance to ensure geomorphic and ecological resilience worldwide. Full article

The ultimate goal of the secession movements is to gain recognition in accordance with international law, thereby strengthening its legitimacy on the international stage. The intensity of the conflict influences the likelihood of the movement being addressed within the framework of Countering Violent Extremism (CVE) discourses, thereby shaping its international legitimacy. This article examines how the international legitimacy of secession movements is shaped by conflict profiles, religious significance and the CVE framework. Moving beyond the tendency to treat long-standing separatist conflicts as inherently religious, religion can enter separatist conflicts in various ways, such as being an indicator of collective differentiation, a language of mobilization, a source of symbolic legitimacy or an external framework of interpretation. In this study, international legitimacy is conceptualized as existing beyond formal recognition and is assessed using four indicators: discursive, diplomatic, institutional and support-based legitimacy. Five African case studies (Western Sahara, Cabinda, Biafra, Azawad and Ogaden) are detailed within the context of these indicators, having been selected for sharing similar values regarding conflict based on variables derived from the Uppsala Conflict Data Program (UCDP). Ultimately, in the cases of Western Sahara and Azawad, associating religion with extremism undermines the legitimacy of separatist claims and restricts access to international policy and military support. Full article

Extreme hydrological events fundamentally alter sediment transport dynamics across grain, reach, and watershed scales, rendering classical equilibrium-based transport formulations inadequate. This review synthesizes recent advances in multiscale sediment transport modeling under highly unsteady and high-magnitude forcing conditions. At the grain scale, particle-resolved simulations demonstrate that sediment entrainment is governed by turbulence intermittency and transient force exceedance rather than mean bed shear stress thresholds, particularly when the hydrograph rise timescale (T_h) becomes comparable to particle response times (T_p). At the reach scale, non-equilibrium transport emerges when the unsteadiness ratio T_h/T_a∼O(1), where T_a is the sediment adaptation timescale representing the time required for sediment flux to adjust toward transport capacity. Under these conditions, pronounced hysteresis between discharge and sediment flux is observed, requiring relaxation-based transport formulations instead of instantaneous equilibrium laws. At the watershed scale, the sediment delivery ratio (SDR), defined as the ratio of sediment yield at the basin outlet to total hillslope erosion, becomes highly time-dependent. Extreme precipitation events can activate hillslope-channel connectivity, increasing SDR by orders of magnitude relative to baseline conditions. A unified dimensionless scaling framework is presented based on mobility intensity (θ/θ_c, where θ is the Shields parameter and θ_c is its critical value for incipient motion), unsteadiness ratio (T_h/T_a), and morphodynamic coupling (T_f/T_m, where T_f is the hydraulic advection timescale and T_m is the morphodynamic adjustment timescale). This framework enables classification of sediment transport regimes ranging from quasi-equilibrium to cascade-dominated states. The synthesis demonstrates that predictive uncertainty increases nonlinearly across scales due to timescale compression, threshold activation, and feedback between flow hydraulics and evolving morphology. Recent developments in hybrid physics-AI approaches show promise in improving predictive capability by enabling dynamic transport closures, surrogate modeling of computationally expensive microscale processes, and data assimilation for real-time forecasting. However, these approaches remain limited by extrapolation uncertainty and the need to enforce physical constraints. Overall, this review concludes that regime-aware multiscale coupling, combined with uncertainty quantification and adaptive modeling strategies, is essential for robust sediment hazard prediction and climate-resilient infrastructure design under intensifying hydrological extremes. Full article

Managed pressure drilling (MPD) is the core technology for developing formations with high pressure and narrow density windows. It precisely maintains the bottomhole pressure (BHP) within the safe operating window defined by formation pore pressure and fracture pressure by actively regulating the wellbore pressure profile. If pressure control becomes unstable, it can easily trigger gas kicks or lost circulation, posing a severe threat to operational safety. However, existing model predictive control (MPC) schemes have significant limitations: pure data-driven models exhibit poor generalization under complex conditions, while control algorithms based on traditional mechanistic models struggle to meet the stringent real-time requirements of field control cycles due to high-complexity numerical iteration processes. To balance control precision and real-time performance, this paper proposes a physics-informed model predictive control framework (PINC-MPC). During the training phase, physical prior knowledge such as the law of mass conservation is embedded into the neural network as constraints to construct a physically consistent deep surrogate model, enabling it to characterize complex wellbore characteristics. In the control phase, this surrogate model replaces the time-consuming numerical solving process of the mechanistic model within the MPC loop, achieving near-real-time state prediction and rolling optimization while ensuring physical fidelity. Experimental results indicate that PINC-MPC demonstrates superior control performance. Its median single-step solving time is only 16.81 ms, achieving an 11.1-fold acceleration compared to the mechanistic model-based scheme (187.3 ms). In a 5000 s full-cycle closed-loop control experiment, the total time required for the former is only 1.68 s, while the latter reaches 18.73 s, representing an efficiency improvement of approximately 91%. In terms of control accuracy, the integrated absolute error (IAE), reflecting the total deviation of the control process, significantly decreased from 63.40 MPa·s for the industrial successive linearization MPC (SLMPC) to 12.90 MPa·s, an improvement of 79.7%. Especially in extreme dynamic conditions such as simulated pump shutdowns for pipe connections and sudden gas kicks, the framework demonstrates excellent predictive ability and response efficiency. It can proactively trigger compensation actions to keep BHP fluctuations within 0.30 MPa, significantly outperforming the traditional SLMPC method. The research results prove that PINC-MPC provides an efficient, precise, and robust nonlinear control strategy for MPD systems, offering important engineering reference value for enhancing the automation level of intelligent drilling systems. Full article