Abstract

The teaching and practical use of vector calculus in engineering often face challenges rooted in mathematical abstraction and the limited availability of tools capable of supporting three-dimensional geometric analysis. These constraints hinder precision when designing complex structural surfaces. Addressing this gap, the present study proposes the development and implementation of an interactive computational tool—built in MATLAB App Designer that integrates vector-based formulations with numerical methods to parameterize, visualize, and compute the surface area of three-dimensional geometries, with a particular focus on sizing geomembranes for circular aquaculture ponds. The research methodology comprised theoretical, numerical, and experimental components. Exact vector parameterizations were formulated, symbolic integration and discretization algorithms were implemented, and the resulting computations were assessed through error estimation and convergence analysis. The findings demonstrate a close match between analytical and numerical solutions, with relative errors below 0.1%, stable computational behavior under moderate discretization settings, and distortion-free threedimensional visualizations. Overall, the study shows that combining exact vector modeling with adaptive numerical techniques and interactive visualization provides an efficient and low-cost framework for surface-area computation and structural design. This approach offers a practical alternative to conventional CAD platforms and delivers meaningful benefits for both engineering education and industrial applications within sustainable production systems.OPEN ACCESS Received: 10/11/2025 Accepted: 14/01/2026 Published: 16/04/2026

Abstract

The conventional approach to drill bit selection primarily relies on the performance records of bits used in adjacent wells, where the bestperforming bit in each formation is selected for the corresponding zone to be drilled. However, this method does not take into account the lithology and rock mechanical properties of all relevant wells, nor can it evaluate the adaptability of a particular bit type to different intervals. As a result, it fails to fully ensure an optimal match between the bit and the formation, thus exhibiting significant limitations. To address these issues, this paper proposes a bit optimization method based on grey clustering and grey correlation analysis. This method comprehensively considers the influence of rock mechanics parameters on formation drillability and quantitatively evaluates the similarity in drilling resistance between the target formation and previously drilled intervals using grey clustering. This approach breaks away from the traditional constraint of limited bit options for a specific formation grade. Instead, it screens all previously used bit types to construct a candidate bit library for the target zone. Subsequently, the grey correlation method is applied to assess the candidate bits using multiple indicators that reflect bit performance. This enables the optimization of bit types for various target zones. Field applications demonstrate that the new bit selection method effectively improves upon the conventional practices by enhancing the flexibility and scientific basis of bit selection, and has yielded favorable results in actual drilling operations.OPEN ACCESS Received: 28/07/2025 Accepted: 16/10/2025 Published: 23/01/2026

Abstract

During repeated mining of shallow and closely spaced coal seams, the failure of coal pillars within the upper goaf can induce dynamic hazards—such as shield jamming, support collapse, and roof fall—at the lower fully mechanized working face. To assess the stability of composite bearing structures, this study adopts a comprehensive approach that integrates orthogonal experimental design with single-factor experiments, supported by numerical simulation methods. Firstly, a composite bearing structure model is developed based on the engineering conditions of the Xingelao Coal Mine, followed by a comprehensive mechanical analysis. Secondly, experimental variables such as cement content, fly ash content, river sand content, and solid/slurry concentration are considered to systematically analyze their impact on backfill strength through proportion adjustment experiments. Furthermore, the controlled variable method is applied to adjust the backfill ratio, ultimately determining the optimal backfill mix ratio S8-2/3, which demonstrates a 7-day uniaxial compressive strength of 2.70 MPa with a backfill ratio of 2/3. This ratio satisfies both the mine’s strength requirements and cost-effectiveness criteria. Based on this, a failure model for the “backfill-pillar” composite bearing structure is established by integrating stress-strain curves with observed failure modes during load-bearing processes. Finally, numerical simulation software was utilized to perform a stability analysis on both the composite load-bearing structure formed by post-backfilling in the roomand-pillar goaf and the overlying strata of the mined-out area. Numerical simulation results indicate that, under repeated mining conditions, the use of S8-2/3 backfilling material in room-and-pillar goafs significantly enhances the load-bearing capacity of residual coal pillars. It also effectively controls overburden movement and supports the safe and efficient extraction of coal resources.OPEN ACCESS Received: 23/04/2025 Accepted: 16/06/2025 Published: 22/09/2025

Abstract

Climate change demands innovative renewable energy solutions, with wind energy emerging as a key resource. Vertical-axis wind turbines (VAWTs) are particularly suited for low-speed, turbulent wind environments due to their ability to capture wind from all directions. However, VAWTs face aerodynamic difficulties, especially at the downwind, where problems like negative torque and decreased efficiency are frequent. This study explores a novel solution for enhancing VAWT performance by incorporating an inner cylindrical deflector aimed at optimizing airflow around the blades. Using computational fluid dynamics (CFD) simulations, the study focuses on a three-bladed H-type VAWT with an airfoil profile of NACA0018 at a turbine diameter of 1 m. The simulations begin by evaluating a bare turbine arrangement, which shows negative torque beginning at an azimuth angle of about 165 degrees onwards. When a cylindrical deflector of different diameters is introduced, the torque coefficient and overall performance are greatly enhanced by a 0.3-meter diameter. The cylindrical deflector’s effectiveness is demonstrated by the 15% increase in power coefficient C pthat results from its inclusion. These results highlight how an inner cylindrical deflector could be a useful addition to VAWTs, resolving significant inefficiencies while preserving a positive angle of attack. This strategy offers a way forward for more effective VAWT designs in renewable energy systems in addition to increasing energy output. To enhance the efficiency of vertical-axis wind turbines (VAWTs) for both urban and rural applications, future research could investigate different configurations and empirically confirm these findings.OPEN ACCESS Received: 21/01/2025 Accepted: 10/03/2025 Published: 20/04/2025

Abstract

Underwater images play a critical role in underwater exploration and related tasks. However, due to light attenuation and other underwater factors, underwater images often suffer from color distortion and low contrast, which to some extent limit the efficiency and safety of underwater exploration. To meticulously address these issues and enhance the accuracy and reliability of underwater exploration, this paper proposes a multi-task underwater image enhancement method based on Retinex theory. This method divides the underwater image enhancement task into several sub-tasks, including image decomposition, color correction, detail reconstruction, and illumination adjustment. Specialized sub-networks— DecomNet, DecolorNet, and DelightNet—are designed to specifically address these problems, thereby alleviating color distortion, enhancing image details, and improving contrast. Experiments conducted on several publicly underwater image datasets indicate that the quality of underwater images is significantly improved after enhancement with the proposed method, compared to other representative underwater image processing techniques. For example, on the real-world dataset Underwater Image Enhancement Benchmark, the MSE, Structural Similarity Index Measure, and Peak signal-to-noise ratio scores achieved were 453.480, 0.901, and 25.145, respectively. This study holds significant implications for underwater exploration, with potential applications in the fields of marine research and underwater archaeology.OPEN ACCESS Received: 03/11/2024 Accepted: 27/12/2024 Published: 20/04/2025

Abstract

The indicators-coupled grey relational analysis (ICGRA) models are important in clustering panel data with cross-sectional dependence. However, there is still little research on performance validation for the various ICGRA models. In this paper, we investigate the performance of the existing ICGRA models accounting for the reordering of indicators. Firstly, the robot execution failures (REF) dataset of the University of California Irvine (UCI) machine learning database is adopted to validate the robustness of four traditional ICGRA models. Then, we compared the grey relational orders for all arrangements of indicators in panel data. Simulation experiments showed that the four ICGRA models are not all robust against the grey relational order. To resolve this problem, we adopted the mean value theory and deep modeling to optimize the four models and compared them with the tetrahedral grey relational analysis (GRA) model that considers the coupling effect between indicators on the grey relational order, as well as with the k-nearest neighbor (KNN) algorithm. Results show that the classification accuracy of the averaged absolute GRA model was 97.73%, the other optimized ICGRA models and the k-nearest neighbor (KNN) method all achieved 100% accuracy, while the tetrahedral GRA model has an accuracy of 83.33%. Therefore, the average grey incidence degree for all arrangements of indicators and deep modeling significantly improves the stability of models and enhances the clustering accuracy in different cases.OPEN ACCESS Received: 09/12/2024 Accepted: 04/03/2025 Published: 30/06/2025