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Research Article
A Unified Framework for Prolonged Winter Cold Extremes: Downward Coupling of Stratospheric Vortex Splits and Tropospheric Quasi-stationary Wave Amplification
Belay Sitotaw Goshu*
Issue:
Volume 1, Issue 2, June 2026
Pages:
88-101
Received:
23 February 2026
Accepted:
4 March 2026
Published:
16 March 2026
Abstract: Background: Polar vortex splits, a subset of sudden stratospheric warming, can drive extreme midlatitude cold outbreaks by coupling stratospheric disruptions downward to the troposphere. However, surface impacts vary widely, with some events producing severe, persistent cold and others remaining benign, highlighting the need to distinguish underlying dynamical pathways. Purpose: This study aims to quantify the spectrum of surface cold impacts from historical polar vortex splits and to elucidate the key tropospheric and stratospheric mechanisms that differentiate high-impact synergistic (wave-amplified) events from low-impact zonal-background events. Methods: Thirty synthetic vortex split events (1958–2023) were identified from reanalysis data and composited into synergistic and zonal categories. Lagged composites (Days –10 to +20 relative to onset) of potential vorticity, geopotential height, temperature, sea-level pressure, zonal winds, Eliassen-Palm flux, wave amplitude, jet latitude, blocking index, and storm-track activity were analyzed to reveal dynamical contrasts. Novelty: The work provides the first systematic, quantitative comparison of synergistic versus zonal split composites, explicitly linking tropospheric–stratospheric wave interference, jet buckling, persistent blocking, and focused wave breaking to explain heterogeneous surface outcomes. Findings: Synergistic splits produce 4–5× stronger cold anomalies (peak –10.5°C vs. –2.0°C), greater spatial extent (14.4% NH coverage), and longer persistence (~4 days) than zonal splits, driven by constructive wave reinforcement (1.8–5.3× amplification), southward jet displacement (~2°), sustained Greenland blocking (≥4 days), enhanced downstream storm tracks (correlation –0.69), and EP-flux divergence/convergence patterns favoring prolonged negative NAM/NAO responses. Conclusion: Tropospheric planetary wave preconditioning and synergistic coupling, rather than the stratospheric split alone, governs the severity of surface cold extremes. Recommendation: Incorporate real-time wave-precursor diagnostics into forecasting systems and expand analyses with large-ensemble simulations to assess future changes in split-related extreme weather risk.
Abstract: Background: Polar vortex splits, a subset of sudden stratospheric warming, can drive extreme midlatitude cold outbreaks by coupling stratospheric disruptions downward to the troposphere. However, surface impacts vary widely, with some events producing severe, persistent cold and others remaining benign, highlighting the need to distinguish underlyi...
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Research Article
Theoretical Substantiation of the Parameters of a
Roller-leveller for Soil Crust Softening
Nodirbek Egamov*
Issue:
Volume 1, Issue 2, June 2026
Pages:
102-107
Received:
26 February 2026
Accepted:
9 March 2026
Published:
19 March 2026
Abstract: This article provides a comprehensive theoretical basis for determining the structural and technological parameters of a roller-type working body, which is specifically designed to loosen the crust that forms on the inter-row soil of cotton crops. The formation of a dense soil crust in cotton fields negatively affects the emergence and growth of seedlings, leading to uneven plant development and potentially reduced crop yields. To mitigate these negative effects and ensure uniform and complete emergence of cotton seedlings, this study focused on identifying key operational parameters of the roller. Among these parameters are the depth of soil penetration by the roller, the large and small diameters of the roller, the number of hexagonal prongs installed on the roller surface, the magnitude of the vertical load applied during operation, and the tension force of the pressure spring that regulates the roller’s interaction with the soil. Based on rigorous theoretical research and analysis, the optimal values of the roller's geometric and force parameters were established. These optimal values are determined under the condition that the crust is completely and efficiently loosened while minimizing energy expenditure and mechanical stress on the roller components. The study also takes into account the interaction between the roller and varying soil types, ensuring that the roller’s design is versatile and capable of maintaining high-quality performance under diverse field conditions. The findings of this study have practical significance for the improvement of working bodies used in cotton cultivation, particularly for cultivators and other soil-processing machinery. By applying the determined parameters, agricultural engineers and practitioners can enhance the operational efficiency of their equipment, reduce labor and energy costs, and achieve better soil preparation for cotton seedlings. Furthermore, this research contributes to the development of energy- and resource-efficient agricultural technologies, supporting sustainable farming practices. The results serve as a scientific foundation for future design improvements and technological advancements in soil cultivation machinery, ensuring that both productivity and quality are maximized in cotton production.
Abstract: This article provides a comprehensive theoretical basis for determining the structural and technological parameters of a roller-type working body, which is specifically designed to loosen the crust that forms on the inter-row soil of cotton crops. The formation of a dense soil crust in cotton fields negatively affects the emergence and growth of se...
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Research Article
Application of Partial Stress Release Method by Parallel Drilling to Determine the Initial Plane Stress State of Rock Mass Using Hole Diameter Variation Method
Issue:
Volume 1, Issue 2, June 2026
Pages:
108-117
Received:
13 October 2025
Accepted:
4 February 2026
Published:
23 April 2026
Abstract: To use the hole diameter variation method using the partial stress release method by parallel drilling in determining the initial plane stress state of the rock mass, the stress state change surrounding the main measuring hole by parallel drilling is derived and the results are verified by numerical simulations. In the partial stress relief method based on parallel drilling method, firstly, the main measurement hole is drilled to appropriate depth, and then the sensor which is designed and developed to measure the diametrical deformations in three different directions (in general they are apart 120° from each other) is installed in the main hole to sense the hole diametrical change, and then multiple parallel stress relief holes (approximately four at 90°) are drilled at regular intervals next to the core without drilling a larger hole which has coincident center with the main hole as in the complete stress relief method. The diametrical change due to the release of stress surrounding the main measuring hole is then measured using the diametrical deformation gage and through them the initial plane stress state of the rock mass is determined. The numerical simulation results of this method show that the reliability of the partial stress release method compared to the full stress release method can reach more than 99%.
Abstract: To use the hole diameter variation method using the partial stress release method by parallel drilling in determining the initial plane stress state of the rock mass, the stress state change surrounding the main measuring hole by parallel drilling is derived and the results are verified by numerical simulations. In the partial stress relief method ...
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Methodology Article
Physics-informed Neural Networks for Solving
Second-order Boundary Value Problems Comparison with FEM, FD Methods
Ujjal Mandal*
Issue:
Volume 1, Issue 2, June 2026
Pages:
118-130
Received:
4 April 2026
Accepted:
16 April 2026
Published:
24 April 2026
Abstract: Recently, physics-informed neural networks (PINNs) have become an encouraging computational approach to solving differential equations through the use of an explicit encoding of physical laws into the learning step of neural networks. The paper carries out a detailed comparison and contrast of PINNs with two well-established numerical approaches, i.e., Finite Element Method (FEM) and Finite Difference (FD) method in terms of solving second-order, boundary value problems. It is assumed to be a representative benchmark problem defined over a bounded domain with given boundary conditions, and to which an analytical solution exists to evaluate the accuracy of the numerical methods. The suggested PINN framework is built as a feedforward neural network framework that has a trial solution strategy that provides a natural way to meet the boundary conditions. Automatic differentiation is used to calculate necessary derivatives effectively and precisely so as not to require numerical approximation schemes. In the training, the L-BFGS optimization algorithm is used along with the Sobol quasi-random collocation points to guarantee efficient sampling of the computational domain and enhance the convergence characteristics. Moreover, mathematical underpinnings of the PINN formulation, as well as loss function construction and training mechanisms are addressed and compared with the respective formulations in FEM and FD methods. A large number of numerical experiments are performed to test the performance of the three methods in terms of accuracy, convergence properties, and computational efficiency. The findings reveal that PINNs are as accurate as classical numerical methods with a number of benefits, including mesh-free nature, ability to work with complex domains, and the natural implementation of physical constraints. Whereas FEM and FD approaches are more efficient when it comes to solving low-dimensional problems, PINNs offer a more general framework that can be applied to more complicated cases and the higher dimensionality. In general, in this paper, the promise of physics-informed learning as a well-built and versatile substitute to the conventional numerical approaches is emphasized. The results can be added to the existing literature on the relevance of PINNs to computational physics and engineering, especially those problems where traditional methods are limited by geometric complexity or data integration needs.
Abstract: Recently, physics-informed neural networks (PINNs) have become an encouraging computational approach to solving differential equations through the use of an explicit encoding of physical laws into the learning step of neural networks. The paper carries out a detailed comparison and contrast of PINNs with two well-established numerical approaches, i...
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