Search Results (237)

This study presents a detailed analysis of the influence of hole presence and size on the behavior of CFRP composite plates subjected to axial compression. The plates were manufactured by an autoclave method from eight-ply laminate in a symmetrical fiber arrangement [45°/−45°/90°/0°₂/90°/−45°/45°]. Four central hole plates of 0 mm (reference), 2 mm, 4 mm, and 8 mm in diameter were analyzed. Tests were conducted using a Cometech universal testing machine in combination with the ARAMIS digital image correlation (DIC) system, enabling the non-contact measurement of real-time displacements and local deformations in the region of interest. The novel feature of this work was its dual use of independent measurement methods—machine-based and DIC-based—allowing for the assessment of boundary condition effects and grip slippage on failure load accuracy. The experiments were carried out until complete structural failure, enabling a post-critical analysis of material behavior and failure modes for different geometric configurations. The study investigated load–deflection and load–shortening curves, failure mechanisms, and ultimate loads. The results showed that the presence of a hole leads to localized deformation, a change in the failure mode, and a nonlinear reduction in load-carrying capacity—by approximately 30% for the largest hole. These findings provide complementary data for the design of thin-walled composite components with technological openings and serve as a robust reference for numerical model validation. Full article

Single-image super-resolution (SISR) plays a critical role in enhancing visual quality for real-world applications, including industrial inspection and embedded vision systems. While deep learning-based approaches have made significant progress in SR, existing lightweight SR models often fail to accurately reconstruct high-frequency textures, especially under complex degradation scenarios, resulting in blurry edges and structural artifacts. To address this challenge, we propose a Dense Residual Fused Attention Network (DRFAN), a novel lightweight hybrid architecture designed to enhance high-frequency texture recovery in challenging degradation conditions. Moreover, by coupling convolutional layers and attention mechanisms through gated interaction modules, the DRFAN enhances local details and global dependencies with linear computational complexity, enabling the efficient utilization of multi-level spatial information while effectively alleviating the loss of high-frequency texture details. To evaluate its effectiveness, we conducted ×4 super-resolution experiments on five public benchmarks. The DRFAN achieves the best performance among all compared lightweight models. Visual comparisons show that the DRFAN restores more accurate geometric structures, with up to +1.2 dB/+0.0281 SSIM gain over SwinIR-S on Urban100 samples. Additionally, on a domain-specific rice grain dataset, the DRFAN outperforms SwinIR-S by +0.19 dB in PSNR and +0.0015 in SSIM, restoring clearer textures and grain boundaries essential for industrial quality inspection. The proposed method provides a compelling balance between model complexity and image reconstruction fidelity, making it well-suited for deployment in resource-constrained visual systems and industrial applications. Full article

The compressive capacity of helical anchors constitutes a pivotal performance parameter in geotechnical design. To precisely predict the compressive bearing behavior of helical anchors in aeolian sand, this study integrates in situ testing with finite element numerical analysis to systematically elucidate the non-linear evolution of its load-bearing mechanisms. The XGBoost algorithm enabled the rigorous quantification of the governing geometric features of compressive capacity, culminating in a computational framework for the bearing capacity factor (N_q) and lateral earth pressure coefficient (K_u). The research findings demonstrate the following: (1) Compressive capacity exhibits significant enhancement with increasing helix diameter yet displays limited sensitivity to helix number. (2) Load–displacement curves progress through three distinct phases—initial quasi-linear, intermediate non-linear, and terminal quasi-linear stages—under escalating pressure. (3) At embedment depths of H < 5D, tensile capacity diminishes by approximately 80% relative to compressive capacity, manifesting as characteristic shallow anchor failure patterns. (4) When H ≥ 5D, stress redistribution transitions from bowl-shaped to elliptical contours, with ≤10% divergence between uplift/compressive capacities, establishing 5D as the critical threshold defining shallow versus deep anchor behavior. (5) The helix spacing ratio (S/D) governs the failure mode transition, where cylindrical shear (CS) dominates at S/D ≤ 4, while individual bearing (IB) prevails at S/D > 4. (6) XGBoost feature importance analysis confirms internal friction angle, helix diameter, and embedment depth as the three parameters exerting the most pronounced influence on capacity. (7) The proposed computational models for N_q and K_u demonstrate exceptional concordance with numerical simulations (mean deviation = 1.03, variance = 0.012). These outcomes provide both theoretical foundations and practical methodologies for helical anchor engineering in aeolian sand environments. Full article

This study systematically investigates the axial compression capacity calculation method for 7075-T6 aluminum alloy rectangular hollow section (RHS) members based on the Continuous Strength Method (CSM). Axial compression tests were conducted on nine RHS specimens using a YAW-500 electro-hydraulic servo testing machine, and nonlinear finite element models considering material plasticity and geometric imperfections were established using ABAQUS/CAE. The numerical results showed good agreement with experimental data, verifying the model’s reliability. Parametric analysis was then performed on RHS members, leading to the development of a CSM-based capacity calculation method and a modified curve for predicting the stability reduction factors of square hollow section members. The approach combining this modified curve with Chinese codes is termed the Modified Chinese Code Method. The axial capacities calculated by the CSM-based method, Modified Chinese Code Method, EN 1999-1-1, and AASTM were compared for accuracy evaluation. The conclusions indicate that the proposed modified curve provides more accurate predictions of stability coefficients for square tubes, and the CSM-based method yields more precise capacity predictions than existing international design codes, though it may overestimate the capacity for Class 4 cross-section members and thus requires further refinement. Full article

Prompt and precise cropland mapping is indispensable for safeguarding food security, enhancing land resource utilization, and advancing sustainable agricultural practices. Conventional approaches faced difficulties in complex terrain marked by fragmented plots, pronounced elevation differences, and non-uniform field borders. To address these challenges, we propose DAENet, a novel deep learning framework designed for accurate cropland extraction from high-resolution GaoFen-1 (GF-1) satellite imagery. DAENet employs a novel Geometric-Optimized and Boundary-Restrained (GOBR) Block, which combines channel attention, multi-scale spatial attention, and boundary supervision mechanisms to effectively mitigate challenges arising from disjointed cropland parcels, topography-cast shadows, and indistinct edges. We conducted comparative experiments using 8 mainstream semantic segmentation models. The results demonstrate that DAENet achieves superior performance, with an Intersection over Union (IoU) of 0.9636, representing a 4% improvement over the best-performing baseline, and an F1-score of 0.9811, marking a 2% increase. Ablation analysis further validated the indispensable contribution of GOBR modules in improving segmentation precision. Using our approach, we successfully extracted 25,556.98 hectares of cropland within the study area, encompassing a total of 67,850 individual blocks. Additionally, the proposed method exhibits robust generalization across varying spatial resolutions, underscoring its effectiveness as a high-accuracy solution for agricultural monitoring and sustainable land management in complex terrain. Full article

The provision of food in pre-Inca/Inca cultures (1000 BC–≈1532 AD) in environments near Lake Titikaka (approximately 4000 m above sea level) was possible through an agricultural technique called “Waru-Waru”, which consists of filling the space (volume) between rows of land containing plants that are cultivated (a series of earth platforms surrounded by water canals) with water, using water as thermal energy storage to store energy during the day and to regulate the temperature of the soil and crop atmosphere at night. The problem is that these cultures left no evidence in written documents that have been preserved to this day indicating the mathematical models, the physics involved, and the experimental part they performed for the research, development, and innovation of the “Waru-Waru” technique. From a review of the existing literature, there is (1) bibliography that is devoted to descriptive research (about the geometry, dimensions, and shapes of the crop fields (and more based on archaeological remains that have survived to the present day) and (2) studies presenting complex mathematical models with many physical parameters measured only with recently developed instrumentation. The research objectives of this paper are as follows: (1) develop a mathematical model that uses finite differences in fluid mechanics, thermodynamics, and heat transfer to explain the experimental and theory principles of this pre-Inca/Inca technique; (2) the proposed mathematical model must be in accordance with the mathematical calculation tools available in pre-Inca/Inca cultures (yupana and quipu), which are mainly based on arithmetic operations such as addition, subtraction, and multiplication; (3) develop a mathematical model in a sequence of steps aimed at determining the best geometric form for thermal energy storage and plant cultivation and that has a simple design (easy to transmit between farmers); (4) consider the assumptions necessary for the development of the mathematical model from the point of view of research on the geometry of earth platforms and water channels and their implantation in each cultivation area; (5) transmit knowledge of the construction and maintenance of “Waru-Waru” agricultural technology to farmers who have cultivated these fields since pre-Hispanic times. The main conclusion is that, in the mathematical model developed, algebraic mathematical expressions based on addition and multiplication are obtained to predict and explain the evolution of soil and water temperatures in a specific crop field using crop field characterization parameters for which their values are experimentally determined in the crop area where a “Waru-Waru” is to be built. Therefore, the storage of thermal energy in water allows crops to survive nights with low temperatures, and indirectly, it allows the interpretation that the Inca culture possessed knowledge of mathematics (addition, subtraction, multiplication, finite differences, approximation methods, and the like), physics (fluids, thermodynamics, and heat transfer), and experimentation, with priority given to agricultural techniques (and in general, as observed in all archaeological evidence) that are in-depth, exact, practical, lasting, and easy to transmit. Understanding this sustainable energy storage technique can be useful in the current circumstances of global warming and climate change within the same growing areas and/or in similar climatic and environmental scenarios. This technique can help in reducing the use of fossil or traditional fuels and infrastructure (greenhouses) that generate heat, expanding the agricultural frontier. Full article

The scaling up of carbon capture, utilization, and storage (CCUS) deployment is constrained by multiple factors, including technological immaturity, high capital expenditures, and extended investment return periods. The existing research on CCUS investment decisions predominantly centers on coal-fired power plants, with the utilization pathways placing a primary emphasis on storage or enhanced oil recovery (EOR). There is limited research available regarding the chemical utilization of carbon dioxide (CO₂). This study develops an options-based analytical model, employing geometric Brownian motion to characterize carbon and oil price uncertainties while incorporating the learning curve effect in carbon capture infrastructure costs. Additionally, revenues from chemical utilization and EOR are integrated into the return model. A case study is conducted on a process producing 100,000 tons of methanol annually via CO₂ hydrogenation. Based on numerical simulations, we determine the optimal investment conditions for the “CO₂-to-methanol + EOR” collaborative scheme. Parameter sensitivity analyses further evaluate how key variables—carbon pricing, oil market dynamics, targeted subsidies, and the cost of renewable electricity—influence investment timing and feasibility. The results reveal that the following: (1) Carbon pricing plays a pivotal role in influencing investment decisions related to CCUS. A stable and sufficiently high carbon price improves the economic feasibility of CCUS projects. When the initial carbon price reaches 125 CNY/t or higher, refining–chemical integrated plants are incentivized to make immediate investments. (2) Increases in oil prices also encourage CCUS investment decisions by refining–chemical integrated plants, but the effect is weaker than that of carbon prices. The model reveals that when oil prices exceed USD 134 per barrel, the investment trigger is activated, leading to earlier project implementation. (3) EOR subsidy and the initial equipment investment subsidy can promote investment and bring forward the expected exercise time of the option. Immediate investment conditions will be triggered when EOR subsidy reaches CNY 75 per barrel or more, or the subsidy coefficient reaches 0.2 or higher. (4) The levelized cost of electricity (LCOE) from photovoltaic sources is identified as a key determinant of hydrogen production economics. A sustained decline in LCOE—from CNY 0.30/kWh to 0.22/kWh, and further to 0.12/kWh or below—significantly advances the optimal investment window. When LCOE reaches CNY 0.12/kWh, the project achieves economic viability, enabling investment potentially as early as 2025. This study provides guidance and reference cases for CCUS investment decisions integrating EOR and chemical utilization in China’s refining–chemical integrated plants. Full article

License plate recognition is a computer vision technology that plays a crucial role in intelligent transportation systems and vehicle management. However, in real-world road environments, recognition accuracy significantly decreases due to distortions caused by various viewing angles. In particular, existing systems exhibit severe performance degradation when processing license plate images captured at steep angles. This paper proposes a new approach to solve the license plate recognition problem in such unconstrained environments. To accurately recognize text on distorted license plates, it is crucial to precisely locate the four corners of the plate and correct the distortion. For this purpose, the proposed system incorporates vehicle and license plate detection based on YOLOv8 and integrates a Corner Enhancement Module (CEM) utilizing a Deformable Convolutional Network (DCN) into the model’s neck to ensure robust feature extraction against geometric transformations. Additionally, the system significantly improves corner detection accuracy through parallel ensemble processing of three license plate images: the original and two aspect ratio-adjusted versions (2:1 and 1.5:1). Furthermore, we verified the system’s versatility in real road environments by implementing a real-time license plate recognition system using Raspberry Pi 4 and a camera module. Full article

The instability of mine roadways is significantly influenced by the coupled effects of groundwater seepage and non-uniform loading. These interactions often induce localized plastic deformation and progressive failure, particularly in the roof and sidewall regions. Seepage elevates pore water pressure and deteriorates the mechanical properties of the rock mass, while non-uniform loading leads to stress concentration. The combined effect facilitates the propagation of microcracks and the formation of shear zones, ultimately resulting in localized instability. This initial damage disrupts the mechanical equilibrium and can evolve into severe geohazards, including roof collapse, water inrush, and rockburst. Therefore, understanding the damage and failure mechanisms of mine roadways at the mesoscale, under the combined influence of stress heterogeneity and hydraulic weakening, is of critical importance based on laboratory experiments and numerical simulations. However, the large scale of in situ roadway structures imposes significant constraints on full-scale physical modeling due to limitations in laboratory space and loading capacity. To address these challenges, a straight-wall circular arch roadway was adopted as the geometric prototype, with a total height of 4 m (2 m for the straight wall and 2 m for the arch), a base width of 4 m, and an arch radius of 2 m. Scaled physical models were fabricated based on geometric similarity principles, using defect-bearing sandstone specimens with dimensions of 100 mm × 30 mm × 100 mm (length × width × height) and pore-type defects measuring 40 mm × 20 mm × 20 mm (base × wall height × arch radius), to replicate the stress distribution and deformation behavior of the prototype. Uniaxial compression tests on water-saturated sandstone specimens were performed using a TAW-2000 electro-hydraulic servo testing system. The failure process was continuously monitored through acoustic emission (AE) techniques and static strain acquisition systems. Concurrently, FLAC^3D 6.0 numerical simulations were employed to analyze the evolution of internal stress fields and the spatial distribution of plastic zones in saturated sandstone containing pore defects. Experimental results indicate that under non-uniform loading, the stress–strain curves of saturated sandstone with pore-type defects typically exhibit four distinct deformation stages. The extent of crack initiation, propagation, and coalescence is strongly correlated with the magnitude and heterogeneity of localized stress concentrations. AE parameters, including ringing counts and peak frequencies, reveal pronounced spatial partitioning. The internal stress field exhibits an overall banded pattern, with localized variations induced by stress anisotropy. Numerical simulation results further show that shear failure zones tend to cluster regionally, while tensile failure zones are more evenly distributed. Additionally, the stress field configuration at the specimen crown significantly influences the dispersion characteristics of the stress–strain response. These findings offer valuable theoretical insights and practical guidance for surrounding rock control, early warning systems, and reinforcement strategies in water-infiltrated mine roadways subjected to non-uniform loading conditions. Full article

Soil salinization represents a significant global environmental challenge, necessitating the urgent amelioration of saline–alkali lands. As a critical functional component of the soil system, soil aggregates play a pivotal role in enhancing soil structure and are essential for nutrient cycling and plant growth. However, the synergistic effects of plants and microorganisms on alterations in soil aggregate composition, stability, and nutrient content in saline–alkali soils remain inadequately understood. In this study, three saline soil gradients from the Yellow River Delta were analyzed: low saline soil (S1, 1.65 g/kg), medium saline soil (S2, 4.54 g/kg), and high saline soil (S3, 6.57 g/kg). For each gradient, four experimental treatments were established: (1) inoculation of Bacillus tropicus YJ33 alone (B), (2) planting of alfalfa alone (M), (3) combined alfalfa cultivation with B. tropicus YJ33 inoculation (MB), and (4) an unamended control (CK). These treatments were implemented in controlled laboratory pot experiments to evaluate the individual and synergistic impacts of alfalfa and B. tropicus YJ33 on saline soil aggregate stability and structural organization. Overall, B. tropicus YJ33 inoculation significantly promoted the growth and nutritional quality of alfalfa. B, M, and MB treatment increased the contents of total carbon (TC), total nitrogen (TN), and available phosphorus (AP) and promoted the activities of soil alkaline phosphatase (S-ALP) and soil urease (S-UE) in the soil. Simultaneously, these treatments resulted in a reduction in the proportion of micro-aggregates, an increase in the proportion of large and small aggregates, and significantly enhanced mean weight diameter (MWD) and geometric mean diameter (GMD), improving the stability of soil aggregates. Random forest analysis identified AP, B. tropicus YJ33, salinity, TC, and available nitrogen (AN) as key determinants of alfalfa biomass. Partial least squares (PLS) modeling further corroborated the role of B. tropicus YJ33 in enhancing soil nutrient content, improving aggregate stability, and increasing alfalfa yield. In conclusion, B. tropicus YJ33 was demonstrated to enhance the stability of soil aggregates and nutrient availability in saline–alkali soils, thereby significantly promoting the growth, yield, and nutritional quality of alfalfa. Full article

Lamb waves offer a series of desirable features for Structural Health Monitoring (SHM) applications, such as the ability to detect small defects, allowing to detect damage at early stages of its evolution. On the downside, their propagation through media with multiple geometrical features results in complicated patterns, which complicate the task of damage detection, thus hindering the realization of their full potential. This is exacerbated by the fact that numerical models for Lamb waves, which could aid in both the prediction and interpretation of such patterns, are computationally expensive. The present paper provides a flexible surrogate to rapidly evaluate the sensor response in scenarios where Lamb waves propagate in plates that include multiple features or defects. To this end, an offline–online ray tracing approach is combined with Frequency Response Functions (FRFs) and transmissibility functions. Each ray is thereby represented either by a parametrized FRFs, if the origin of the ray lies in the actuator, or by a parametrized transmissibility function, if the origin of the ray lies in a feature. By exploiting the mechanical properties of propagating waves, it is possible to minimize the number of training simulations needed for the surrogate, thus avoiding the repeated evaluation of large models. The efficiency of the surrogate is demonstrated numerically, through an example, including different types of features, in particular through holes and notches, which result in both reflection and conversion of incident waves. For most sensor locations, the surrogate achieves an error between 1% and 4%, while providing a computational speedup of three to four orders of magnitude. Full article

Seed morphological description requires quantitative methods for further comparison. Here, traditional measurements, curvature analysis, and the J-index (percentage of similarity to a geometric model) were applied to the average contours (Acs) of 271 Vitis cultivars from the Spanish collection at IMIDRA (Madrid, Spain), including 9 different Vitis species and several sylvestris seeds (i.e., those derived from plants that once grew in the wild). Acs are graphical representations of the shape in seed populations, which can be obtained either from image analysis programs or computationally opening the way to quantitative analysis. A geometric model is a geometrically defined, closed curve, used as a reference for shape quantification. Based on existing differences between the Hebén cultivar (collected in 2020 and 2024; Hebén model, for morphotype 1) and the European varieties Chenin and Gewurztraminer (Chenin model, for morphotype 2), we created two models. The comparisons were based on a J-index, resulting in four groups: Group 1 contained all seeds with values lower than 90 for both models and included all Vitis species other than V. vinifera and most sylvestris seeds; Groups 2 and 3 contained seeds with J-index values higher than 94 for the Hebén and Chenin models, respectively. Group 4 consisted of seeds not included in the other groups. Based on J-index values, differences in curvature and solidity, and PCA analysis with Fourier coefficients, this work defines two new morphotypes associated with the Hebén (Group 2) and Chenin (Group 3) models, related to Iberian and Western European varieties, respectively. Full article

This study proposes a three-dimensional numerical wave tank (NWT) to calculate wave propagation and hydrodynamic forces based on the Navier–Stokes equation, using commercial Computational Fluid Dynamic (CFD) software ANSYS Fluent. The VOF Method is utilized to identify the free surface. The CFD model employed for generating waves in the NWT is initially verified using analytical theory to evaluate the accuracy of the results. In addition, the User-Defined Function (UDF) in ANSYS Fluent is implemented to ensure the model performs under the oscillatory conditions of the Submerged Horizontal Plate (SHP) Wave Energy Converter (WEC) device, which is localized at the center of the NWT. Finally, the influence of SHP oscillation on the device’s average efficiency was analyzed by comparing seven cases with different geometric configurations, considering both the oscillating and non-oscillating conditions of the SHP under the incidence of different waves. The results indicated that the geometric configuration and wave conditions of Case 4 achieved the best performance, reaching an average efficiency of 35.68%. Full article

Proper geologic reservoir characterization is crucial for energy generation and climate change mitigation efforts. While conventional techniques like core analysis and well logs provide limited spatial reservoir information, seismic data can offer valuable 3D insights into fluid and rock properties away from the well. This research focuses on identifying important structural and stratigraphic variations at the Mississippi Canyon Block 118 (MC-118) field, located on the northern slope of the Gulf of Mexico, which is significantly influenced by complex salt tectonics and slope failure. Due to a lack of direct subsurface data like well logs and cores, this area poses challenges in delineating potential reservoirs for carbon storage. The study leveraged seismic multi-attribute analysis and machine learning on 3-D seismic data and well logs to improve reservoir characterization, which could inform field development strategies for hydrogen or carbon storage. Different combinations of geometric, instantaneous, amplitude-based, spectral frequency, and textural attributes were tested using Self-Organizing Maps (SOM) to identify distinct seismic facies. SOM Models 1 and 2, which combined geometric, spectral, and amplitude-based attributes, were shown to delineate potential storage reservoirs, gas hydrates, salt structures, associated radial faults, and areas with poor data quality due to the presence of the salt structures more than SOM Models 3 and 4. The SOM results presented evidence of potential carbon storage reservoirs and were validated by matching reservoir sands in well log information with identified seismic facies using SOM. By automating data integration and property prediction, the proposed workflow leads to a cost-effective and faster understanding of the subsurface than traditional interpretation methods. Additionally, this approach may apply to other locations with sparse direct subsurface information to identify potential reservoirs of interest. Full article

Map groups exhibit distinct spatial distribution characteristics, making their pattern recognition crucial for map generalization, map matching, geographic dataset construction, and urban planning/analysis. Current pattern recognition methods for map groups primarily fall into two categories: machine learning-based approaches and traditional methods. While both have achieved certain recognition outcomes, they suffer from four key limitations: (1) insufficient algorithmic interpretability; (2) limited model generalizability; (3) restricted pattern diversity in recognition; (4) inability of existing methods (including deep learning and traditional algorithms) to achieve multi-pattern recognition across heterogeneous map group types (e.g., building groups vs. road networks) using a single framework. To address these limitations, this study proposes a graph structure-based multi-pattern recognition algorithm for map groups. The algorithm integrates the quantitative advantages of directional entropy in characterizing spatial distribution patterns with the discriminative power of node degree in analyzing edge-node geometric models. Experimental validation utilized building and road network data from multiple cities, constructing a dataset of 600 samples divided into two subsets: Sample Set 1 (for parameter threshold calibration and rule generation) and Sample Set 2 (for algorithm performance validation and transferability testing). The results demonstrate a classification accuracy of 97% for the proposed algorithm, effectively distinguishing four building group patterns (linear, curved, grid, irregular) and two road network patterns (grid, irregular). This work establishes a novel methodological framework for multi-scale spatial pattern analysis in map generalization and urban planning. Full article