Search Results (774)

This study investigates the settlement characteristics of overlying strata in backfilled stopes at the Sanshandao Gold Mine, focusing on the compaction behavior of backfill materials. Integrating laboratory tests, numerical modeling, and field monitoring, we analyzed the particle size distribution and fractal dimensions of tailings (2.1525) and C material (2.1994), with tailings showing better gradation. Systematic compaction tests examined the effects of mix ratio, water content, and curing time. Results indicate that compression follows a viscous sliding model with exponential curves, progressing through three stages—pore compaction, structural deformation, and elastic/plastic deformation—with energy dissipation ratios of 1:5:18. Water content was the most influential factor, with optimal compaction occurring at 5~8%. Coupled Midas-Flac3D simulations estimated a theoretical compaction rate of 0~2% in filled stopes, excluding seepage and equipment effects. Field monitoring at the −480 m level revealed non-uniform settlement, with maximum subsidence of 63.75 mm above stopes and initial settlement rates of 12~20 mm/month. At the −520 m mining level, the backfill compaction rate reached 0.31%, with minor future increases expected. These findings offer valuable guidance for backfill mixture design and strata control in mining engineering. Full article

Pascal’s triangle is a classical mathematical construct, historically studied for centuries, that organises binomial coefficients in a triangular array and serves as a cornerstone in combinatorics, algebra, and number theory. Herein, we propose to model it with Petri nets, a formal specification technique derived from discrete event systems. A minimal Petri net is created that generates Pascal’s triangle under a simple arithmetic rule. Token counts in each place coincide with binomial coefficients, providing a direct combinatorial interpretation. Two other classical structures emerge from this model: by colouring tokens depending on their parity, the Sierpiński triangle appears; by routing tokens randomly at each branching, the binomial distribution arises, converging to a Gaussian limit as depth increases. As a result, a single Petri construction unifies three mathematical objects: Pascal’s Triangle, Sierpiński’s Triangle, and the Gaussian distribution. This connection illustrates the invaluable potential of Petri nets as unifying tools for modelling discrete mathematical structures and beyond. Full article

The northwestern Hubei region, primarily encompassing Shiyan City and Yunxi County in Hubei Province, constitutes a crucial component of the South Qinling Tectonic Belt. The Neoproterozoic Wudang Group in the study area exhibits Cu element enrichment, with ore deposit formation closely associated with stratigraphic and structural features. This study evaluates copper mineral resource distribution and metallogenic potential in northwestern Hubei by employing factor analysis, concentration-area fractal modeling, and the fuzzy weights-of-evidence method based on stream sediment data, aiming to construct a metallogenic potential model. Factor analysis was applied to process 2002 stream sediment samples of 32 elements to identify principal factors related to copper mineralization. Inverse distance interpolation was used to generate element distribution maps of principal factors, which were integrated with geological and structural data to establish a model using the fuzzy weights of evidence method. Prediction results indicate that most known copper deposits are located within posterior favourability ranges of 0.0027–0.272, constrained by stratigraphic and fault controls. The central northwestern Hubei region is identified as a priority target for future copper exploration. This research provides methodological references for conducting mineral resource potential assessments in north-western Hubei using innovative evaluation approaches. Full article

Pore–throat structure and gas distribution are critical factors in evaluating the quality of tight sandstone reservoirs and hydrocarbon resource potential. Twelve tight sandstone samples from the Lower Permian Shihezi Formation in Hangjin Banner, Ordos Basin, were selected for CTS, X-ray diffraction, HPMI, and gas displacement NMR analyses. By converting the T₂ spectra into pore–throat distributions and applying fractal methods, we quantitatively analyzed the influences of multiple factors on gas distribution characteristics across different pore–throat sizes. The main results are as follows: All samples exhibit a three-stage pore–throat distribution, defining mesopores, micropores, and nanopores; quartz content mainly influences the fractal dimension of mesopores by enhancing structural stability and gas storage capacity, whereas clay minerals control the fractal characteristics of nanopores by increasing pore–throat complexity. An increase in clay mineral content increases the fractal dimension, indicating stronger reservoir heterogeneity and consequently poorer gas-bearing capacity. Larger pore–throat parameters (R_m, S_k, and S_max) correspond to lower fractal dimensions, indicating better connectivity and greater gas storage capacity. Among these factors, pore–throat parameters exert the most significant influence on the fractal dimensions of mesopores and micropores, jointly determining the overall connectivity and the upper limit of the reservoir’s gas-bearing capacity. The results demonstrate that larger pore–throat parameters and higher quartz content help reduce the fractal dimension and enhance the gas-bearing capacity of tight reservoirs. This research enhances understanding of pore–throat structures and gas-bearing capacity in low-permeability reservoirs and provides a theoretical basis for exploration, development, and enhanced recovery in the study area. Full article

The Upper Permian marine shale of the inter-platform basin in the Nanpanjiang Basin are rich in organic matter, widely distributed, and relatively thick, indicating abundant resource potential for hydrocarbon exploration. To clarify the sedimentary condition and the variability of reservoir properties, the paleo-environment was reconstructed by using geochemical, mineralogical, rock-property, and pore-structure data. Building on a lithofacies classification, the development patterns of different shale lithofacies were revealed. Reservoir characteristics among lithofacies were compared using scanning electron microscopy (SEM), nuclear magnetic resonance (NMR), and low-temperature Nuclear Magnetic Resonance Cryoporometry (NMRC) experiments. A fractal analysis was performed based on NMR and NMRC data to quantify pore-scale heterogeneity, calculate fractal dimensions (D₁, D₂, and D_c), and evaluate the complexity of pore systems across lithofacies. Correlation analysis and redundancy analysis were applied to further explore the controlling factors of reservoir heterogeneity. The results showed that organic-rich shale in the Permian Linghao Formation occurred mainly in the 1st Member, with average total organic carbon (TOC) content of 2.57%, and the lower part of the 3rd Member (average TOC content 2.88%). In the 1st Member, high-carbon shale was deposited under humid conditions with intense weathering, abundant fine-grained clastic input from basin margins, strongly reducing (anoxic) bottom waters, vigorous phosphorus recycling, and moderate to low primary productivity. Using TOC and mineral composition, seven shale lithofacies were identified in the Linghao Formation, and their development patterns were established based on depositional paleo-environment characteristics and evolution. In the 1st Member, organic-rich shale was dominated by mixed lithofacies with moderate to high TOC. The paleo-environment exerted a primary control on reservoir properties, gas content, pore structure, and heterogeneity. The high-carbon lithofacies had the most favorable rock properties—higher porosity, greater pore volume, and higher gas content—and contained a larger proportion of well-developed organic pores. Fractal analysis revealed that seepage pores exhibited greater structural complexity than adsorption-related pores, with the high-carbon lithofacies showing the highest overall fractal dimensions and thus the strongest heterogeneity. Across the formation, higher clay content and TOC were the primary drivers of increased pore-scale heterogeneity, whereas greater feldspar and quartz contents tended to diminish it. Carbonates exerted a minor effect. Heterogeneity in adsorption pores exerted the strongest influence on differences among lithofacies. These results highlighted the utility of fractal analysis in quantitatively linking shale mineralogy and organic content to multiscale heterogeneity in inter-platform basin settings. Full article

To reveal the deformation and failure mechanisms as well as the fracture evolution patterns of water-bearing coal–rock composites under complex stress conditions, this study established a true triaxial stress model for the key load-bearing structure of mined coal pillar dams and developed a true triaxial loading apparatus capable of implementing localized unloading paths. True triaxial loading–unloading tests were conducted on coal–rock composites under different water content conditions, and the internal fracture structures were quantitatively characterized using CT scanning combined with fractal analysis. The results indicate that: (1) under a constant axial stress-unloading confining stress path, failure primarily occurs in the coal component, and the extent of failure significantly increases with the water content of the roof rock. For instance, the total fracture volume in the coal body increased by approximately 66% from the dry to the saturated state, while the lateral strain at peak stress decreased by about 65% over the same range, indicating a transition towards more brittle behavior. (2) CT scanning and three-dimensional reconstruction results reveal that the fracture system exhibits pronounced multi-scale polarization, with significant differences in volume, surface area, and morphological parameters between the main fractures and micropores, reflecting strong heterogeneity and anisotropy; (3) fractal dimension analysis of two-dimensional slices indicates that the fracture structures exhibit fractal characteristics in all directions, with the spatial distribution of fractal dimensions closely related to the loading direction. Overall, the XY-direction fractures exhibit the highest complexity, whereas the XZ and YZ directions show pronounced directional anisotropy. As water content increases, the amplitude of fractal dimension fluctuations rises, reflecting an enhancement in the geometric complexity of the fracture system. Full article

Passive acoustic monitoring (PAM) is a valuable tool for ecological research, but many eco-acoustic indices show inconsistent correlations with biodiversity due to methodological variability and environmental noise. We propose a complementary, physically interpretable approach using energy-derived metrics. We analyzed audio recordings from three sites near a major highway in the Ticino River Park (Milan, Italy) using 1 sec equivalent continuous sound pressure level (Leq_1s), peak interval statistics, maximal-overlap discrete-wavelet transform (MODWT), and temporal fractal analysis. This multi-resolution type of approach enabled frequency-specific tracking of acoustic energy and temporal structure. Our results reveal site-specific differences: Site 3, the most distant from the highway, showed higher high-frequency energy and longer temporal persistence, suggesting richer biophonic activity. Site 1, the closest to the highway, displayed flatter spectral profiles and faster autocorrelation decay. Diel patterns were reflected in hourly Leq trends, while fractal analysis revealed frequency- and site-dependent acoustic memory. These automated findings were corroborated by expert annotations of bird activity and traffic. The integration of Leq_1s, peak metrics, and wavelet decomposition offers a suitable framework for soundscape characterization, with strong potential for long-term ecoacoustic monitoring and habitat quality assessment in complex environments. Full article

The growth of intelligent manufacturing systems has led to a wealth of computation-intensive tasks with complex dependencies. These tasks require an efficient offloading architecture that balances responsiveness and energy efficiency across distributed computing resources. Existing task offloading approaches have fundamental limitations when simultaneously optimizing multiple conflicting objectives while accommodating hierarchical computing architectures and heterogeneous resource capabilities. To address these challenges, this paper presents a cloud–fog hierarchical collaborative computing (CFHCC) framework that features fog cluster mechanisms. These methods enable coordinated, multi-node parallel processing while maintaining data sensitivity constraints. The optimization of task distribution across this three-tier architecture is formulated as a multi-objective problem, minimizing both system latency and energy consumption. To solve this problem, a fractal-based multi-objective optimization algorithm is proposed to efficiently explore Pareto-optimal task allocation strategies by employing recursive space partitioning aligned with the hierarchical computing structure. Simulation experiments across varying task scales demonstrate that the proposed method achieves a 20.28% latency reduction and 3.03% energy savings compared to typical and advanced methods for large-scale task scenarios, while also exhibiting superior solution consistency and convergence. A case study on a digital twin manufacturing system validated its practical effectiveness, with CFHCC outperforming traditional cloud–edge collaborative computing by 12.02% in latency and 11.55% in energy consumption, confirming its suitability for diverse intelligent manufacturing applications. Full article

Soil aggregates are critical determinants of soil erosion resistance and nutrient retention capacity, while freeze–thaw cycles (FTCs) induce the structural reorganization of soil aggregates, thereby altering soil stability and influencing soil organic carbon (SOC) sequestration. This study was located in the Minjia River Basin in the typical seasonal freeze–thaw areas of the Loess Plateau and aimed to quantify the effects of FTCs on soil aggregate stability and SOC content under different land use types. Farmland, grassland, and forestland with more than 20 years of usage in the region were selected, and a 0–20 cm soil layer was subjected to seven FTCs (−8 °C to 20 °C), followed by wet and dry sieving classification, focusing on soil aggregate distribution, aggregate stability, mean weight diameter (MWD), geometric mean diameter (GMD), aggregate particle fractal dimension (APD), and SOC content of the aggregate. The results showed that soil aggregates in all land use types were dominated by macroaggregates (>2 mm), with the proportion in forestland (61–63%) > grassland (54–58%) > farmland (38–51%). FTCs enhanced aggregate stability across all land use types, especially in farmland. Concurrently, FTCs reduced the SOC content in all aggregate size fractions, with reduction rates ranging from farmland (9.00–21%) to grassland (4–26%) to forestland (5–31%). Notably, FTCs significantly increased the contribution of 2–5 mm water-stable (WS) aggregates to SOC sequestration, with increment rates of 86% (farmland), 80% (grassland), and 86% (forestland). Furthermore, FTCs altered the correlation between SOC content and aggregate stability. Specifically, the positive correlations of SOC with MWD and GMD were strengthened in aggregates < 0.5 mm but weakened in aggregates >0.5 mm. These findings advance our understanding of the coupled mechanisms underlying soil erosion and carbon cycling across land uses under freeze–thaw, providing a theoretical basis for ecosystem restoration and optimized soil carbon management in cold regions. Full article

As a key indicator for measuring urban green visibility, the Green View Index (GVI) reflects actual visible greenery from a human perspective, playing a vital role in assessing urban greening levels and optimizing green space layouts. Existing studies predominantly rely on single-source remote sensing image analysis or traditional statistical regression methods such as Ordinary Least Squares and Geographically Weighted Regression. These approaches struggle to capture spatial variations in human-perceived greenery at the street level and fail to identify the non-stationary effects of different drivers within localized areas. This study focuses on the Luolong District in the central urban area of Luoyang City, China. Utilizing Baidu Street View imagery and semantic segmentation technology, an automated GVI extraction model was developed to reveal its spatial differentiation characteristics. Spearman correlation analysis and Multiscale Geographically Weighted Regression were employed to identify the dominant drivers of GVI across four dimensions: landscape pattern, vegetation cover, built environment, and accessibility. Field surveys were conducted to validate the findings. The Multiscale Geographically Weighted Regression method allows different variables to have distinct spatial scales of influence in parameter estimation. This approach overcomes the limitations of traditional models in revealing spatial non-stationarity, thereby more accurately characterizing the spatial response mechanism of the Global Vulnerability Index (GVI). Results indicate the following: (1) The study area’s average GVI is 15.24%, reflecting a low overall level with significant spatial variation, exhibiting a “polar core” distribution pattern. (2) Fractal dimension, normalized vegetation index (NDVI), enclosure index, road density, population density, and green space accessibility positively influence GVI, while connectivity index, Euclidean nearest neighbor distance, building density, residential density, and water body accessibility negatively affect it. Among these, NDVI and enclosure index are the most critical factors. (3) Spatial influence scales vary significantly across factors. Euclidean nearest neighbor distance, building density, population density, green space accessibility, and water body accessibility exert global effects on GVI, while fractal dimension, connectivity index, normalized vegetation index, enclosure index, road density, and residential density demonstrate regional dependence. Field survey results confirm that the analytical conclusions align closely with actual greening conditions and socioeconomic characteristics. This study provides data support and decision-making references for green space planning and human habitat optimization in Luoyang City while also offering methodological insights for evaluating urban street green view index and researching ecological spatial equity. Full article

Three-dimensionally (3D)-printed catalytic structures are revolutionizing catalysis and chemical engineering. Unlike traditional supports, modern triply periodic minimal surfaces (TPMS), lattices, and fractals actively influence mass and heat transfer and flow distribution. This review summarizes advancements in the classification, design, fabrication, and application of 3D-printed catalysts over the past decade. The article covers various constructive types (supports, integrated phases, multifunctional reactors) and materials (polymers, ceramics, metals, hybrids), along with fabrication techniques compliant with ISO/ASTM standards (FDM, SLA, DIW, SLM, EBM). It emphasizes post-processing and functionalization strategies (impregnation, calcination, sulfonation) and characterization tools (SAXS, CT, synchrotron-based techniques). A critical comparison highlights advantages, including tunable geometry, improved hydrodynamics, lower pressure drop, enhanced durability, and reproducibility. Three-dimensionally printed catalysts are an interdisciplinary platform combining materials science, chemical engineering, and digital manufacturing. They hold promise for sustainable chemistry, modular production, CO₂ utilization, photocatalysis, and biocatalysis, making them a key innovation for future catalytic reactors. Full article

Organic pores serve as crucial storage spaces for shale gas, whose morphology and structure vary significantly among different types of organic matter, directly influencing the storage and seepage capacity of shale gas. The Upper Permian shale in the Western Hubei Trough formed in diverse sedimentary facies and has undergone multiple geological activities, resulting in strong heterogeneity of organic pores across different strata and regions. To figure out the heterogeneous characteristics of organic pores and the forming reason, the occurrence state of organic matter, pore morphology, and structural parameters (pore size, specific surface area, pore volume, and fractal dimension) of the Upper Permian shale in Western Hubei, have been discussed in detail, based on the data of field emission scanning electron microscopy and low-temperature nitrogen adsorption experiments conducted on the extracted organic matter. On this basis, fractal dimension theory was applied to discuss the heterogeneity of organic pores in different layers, and the reason for heterogeneity has been analyzed in detail. The results indicate that the occurrence mode of organic matter in different layers presents various characteristics: in the Gufeng Formation, the organic matters distribute primarily dispersed in flocculent state; at the bottom of Wujiaping Formation, they occur as isolated individuals, while the organic matters turn into discontinuous laminated distribution in the middle and upper Wujiaping Formation; in the Dalong Formation, the organic matters show continuous parallel banded distribution. Moreover, the morphology and structural parameters of organic pores exhibit obvious changes from the Gufeng Formation to the Dalong Formation: (a) the pore morphology shows the changed trend as extremely complex-simple-complex; (b) the specific surface area and pore volume follow the trend as large-small-large; (c) the pore size distribution displays in the pattern of bimodal-unimodal-bimodal; (d) the data of fractal dimension show the variation of high–low–high. Overall, the various sedimentary environments during the Upper Permian shale depositional period determined the differences in organic sources, which dominated the heterogeneity of organic pores in shale. These data clarify the development and variation characteristics of organic matter pores under different depositional environments, providing a theoretical basis for shale gas exploration and development during the transition from marine to marine–continental facies. Full article

In the observation of tight sandstone cores, the variations in the hydrocarbon charging usually can be observed in the same geological age reservoirs, which manifest as differential oil staining on the core surface. In order to clarify the micro total pore–throat size distribution (TPSD) characteristics and oil content differences of different oil-bearing tight reservoirs, we drilled two types of oil-bearing cores in the Chang6 formation of Xiasiwan Oilfield, conducted casting thin section (CTS), scanning electron microscopy (SEM), and X-ray diffraction (XRD) to qualitatively and quantitatively analyze petrological and pore–throat characteristics. The TPSD of different oil-bearing cores were quantitatively characterized and compared by combining high-pressure mercury injection (HPMI) and constant rate mercury injection (CRMI). Meanwhile, we quantitatively evaluated the complexity of the pore–throat structure based on fractal theory. Our results reveal significant difference in the clay mineral contents between the two types of cores, despite both being classified as arkose. Due to higher contents of illite, calcite, and chlorite, the pores of oil-smelling sandstone are obviously affected by cementation. The result of TPSD characteristics shows that the oil-appearing sandstone samples exhibit well-developed big pores and throats, displaying bimodal distribution, and three-stage fractal characteristics in the TPSD curves. Conversely, oil-smelling sandstone samples manifesting a left-skewed bimodal, pore space contribution of the samples is more dependent on pores and throats smaller than 0.12 μm. The TPSD curves exhibit three-stage and four-stage fractal characteristics. Therefore, the differences in oil-bearing properties between the two types of cores are attributed to variations in mineral composition, diagenesis, clay mineral content, pore types, pore–throat size distribution (PSD), and pore–throat complexity. Our results provide crucial guidance for subsequent reservoir quality assessment in this study area and the development of tight sandstone reservoirs with similar geological characteristics. Full article

Medical image segmentation, driven by the intrinsic fractal characteristics of biological patterns, plays a crucial role in medical image analysis. Recently, universal image segmentation, which aims to build models that generalize robustly to unseen anatomical structures and imaging modalities, has emerged as a promising research direction. To achieve this, previous solutions typically follow the in-context learning (ICL) framework, leveraging segmentation priors from a few labeled in-context references to improve prediction performance on out-of-distribution samples. However, these ICL-based methods often overlook the quality of the in-context set and struggle with capturing intricate anatomical details, thus limiting their segmentation accuracy. To address these issues, we propose VG-SAM, which employs a multi-scale in-context retrieval phase and a visual in-context guided segmentation phase. Specifically, inspired by the hierarchical and self-similar properties in fractal structures, we introduce a multi-level feature similarity strategy to select in-context samples that closely match the query image, thereby ensuring the quality of the in-context samples. In the segmentation phase, we propose to generate multi-granularity visual prompts based on the high-quality priors from the selected in-context set. Following this, these visual prompts, along with the semantic guidance signal derived from the in-context set, are seamlessly integrated into an adaptive fusion module, which effectively guides the Segment Anything Model (SAM) with powerful segmentation capabilities to achieve accurate predictions on out-of-distribution query images. Extensive experiments across multiple datasets demonstrate the effectiveness and superiority of our VG-SAM over the state-of-the-art (SOTA) methods. Notably, under the challenging one-shot reference setting, our VG-SAM surpasses SOTA methods by an average of $6.61\%$ in DSC across all datasets. Full article