Search Results (2,515)

Background: Artificial intelligence (AI) and machine learning (ML) are increasingly used in the diagnosis and management of bone metastases, spanning lesion detection, segmentation, prognostic modeling, fracture risk assessment, and surgical decision support. However, the literature is heterogeneous and rapidly evolving, making it difficult for clinicians to contextualize these developments. Methods: We performed a narrative review of the literature on AI/ML applications in bone metastasis management, focusing on studies that address clinically relevant problems such as detection and segmentation of metastatic lesions, prediction of skeletal-related events and survival, and support for reconstructive decision-making. We prioritized recent, peer-reviewed work that reports model performance and highlights opportunities for clinical translation. Results: Most published studies center on imaging-based diagnosis and lesion segmentation using radiomics and deep learning, with generally high internal performance but limited external validation. Emerging work explores prognostic models and biomechanically informed fracture risk estimation, yet these remain at an early proof-of-concept stage. Very few frameworks are integrated into routine workflows, and explainability, bias mitigation, and health-economic impacts are rarely evaluated. Conclusions: AI and ML tools have substantial potential to standardize imaging assessment, refine risk stratification, and ultimately support personalized management of bone metastases. Future research should focus on externally validated, multimodal models; development of AI-augmented alternatives to the Mirels score; federated multicenter collaboration; and routine incorporation of explainability and cost-effectiveness analyses. Full article

Deep fractured-porous carbonate reservoirs used for underground gas storage (UGS) experience simultaneous changes in temperature and effective stress during cyclic injection and withdrawal, so predicting permeability evolution is essential for evaluating long-term injectivity and deliverability. Using the Xiangguosi UGS as the engineering background, we measured steady-state gas permeability of three fractured-porous carbonate cores under representative conditions (20–80 °C; 15–35 MPa). Permeability decreases nonlinearly under coupled loading: changing temperature or effective stress alone typically reduces permeability by 30–70%, while the maximum reduction under concurrent increases in both variables exceeds 80% relative to the reference condition. An exponential model was fitted to quantify the decay parameter of permeability with effective stress (0.038–0.046 MPa⁻¹) and with temperature (0.016–0.020 °C⁻¹). In addition, the temperature-related exponential decay parameter decreases with increasing effective stress, because compliant fractures and larger pores are progressively pre-closed, weakening the permeability response to temperature. Finally, we propose a parsimonious separable exponential model that reproduces the measurements with a mean relative error below 12%, providing a practical constitutive relation for multiphysics simulations of UGS in fractured-porous carbonates. Full article

Low-permeability waterflooding reservoirs face numerous challenges, including low productivity per well, inadequate formation pressure maintenance, poor waterflood response, and low water injection utilization efficiency. Illustrated by Bai 153 Block in the Changqing Oilfield, the primary concern has shifted in recent years from fracture water breakthrough to formation blockages. Currently, low-yield wells (≤0.5 t) constitute a significant proportion (27.5%), with a recovery factor of only 0.41%. The effectiveness of stimulation treatments is influenced by reservoir properties, treatment types, process parameters, and production performance. Selecting candidate wells requires collecting and analyzing data such as individual well block characteristics. Evaluating treatment effectiveness involves substantial effort and complexity. Early fracturing treatments exhibited significant variations in effectiveness, and the primary controlling factors influencing fracturing success remained unclear. This paper proposes a big data analysis-based method for evaluating stimulation effectiveness in low-permeability waterflooding reservoirs. Utilizing preprocessed geological, construction, and production data from the target block, an integrated application of the Random Forest algorithm and Recursive Feature Elimination ranks the importance of factors affecting treatments and identifies the block’s main controlling factors. Using these factors as target parameters, a multivariate quantitative evaluation model for fracturing effectiveness is established. This model employs the Pearson correlation coefficient method, Recursive Feature Elimination, and the Random Forest algorithm. Results from the quantitative model indicate that the primary main controlling factors that significantly affect post-fracturing oil increment are production parameters, geological parameters such as vertical thickness, fracture pressure, and oil saturation; engineering parameters such as sand ratio, blowout volume, and fracturing method; and production parameters such as pre-measure cumulative fluid production, production months, and pre-measure cumulative oil production, which are most closely related to post-fracturing oil increment. These parameters show the strongest correlation with incremental oil production. The constructed quantitative model demonstrates a linear correlation rate exceeding 85% between predicted fracturing stimulation and actual well test production, verifying its validity. This approach provides a novel method and theoretical foundation for the post-evaluation of oil increment effectiveness from stimulation treatments in low-permeability waterflooding reservoirs. Full article

Seepage–dissolution in carbonate rock fractures serves as the core driver governing the evolution of key engineering projects, including reservoir dam stability, CO₂ geological sequestration, and unstable rock collapse mitigation strategies. While physical heterogeneity (e.g., fracture aperture, mineral distribution) is widely recognized as a critical factor regulating dissolution processes, the specific influence of mineral distribution heterogeneity on dissolution rates still lacks quantitative quantification. To address this gap, this study focuses on limestone fractures and employs multi-component reactive transport numerical simulations to model acidic fluid (pH = 5.0) seepage–dissolution under two Darcy flux conditions (37.8/378 m·yr⁻¹). It investigates the controlling mechanisms of fracture roughness (λ_b = 0.036~0.308) and calcite contents (55%, 75%, 95%) on dissolution dynamics, and analyzes spatial variations in local Darcy velocity, reaction rate, and effective dissolution rate (R_eff,i). Results demonstrate that mineral distribution heterogeneity directly induces pronounced spatial heterogeneity in dissolution behavior: diffusion dominates under low flux (simulation duration: 48.3 days), forming discrete reaction fronts (~15 mm) controlled by mineral clusters; advection prevails under high flux (simulation duration: 4.83 days), generating alternating dissolution–deposition zones (~7.5 mm) with R_eff,i one order of magnitude greater than that under low flux. Notably, 55% calcite content yields the highest R_eff,i (1.87 × 10⁻¹¹ mol·m⁻²·s⁻¹), 0.94 orders of magnitude greater than that at 95% calcite content. A strong linear correlation (R² > 0.98) exists between the Damköhler number (Da_I) and R_eff,i at the same calcite content. Furthermore, the synergistic interaction between fracture aperture and mineral heterogeneity amplifies dissolution complexity, with high roughness (λ_b = 0.308) coupled with 55% calcite content achieving the highest R_eff,i of 2.1 × 10⁻¹¹ mol·m⁻²·s⁻¹. This study provides critical theoretical insights and quantitative data support for fractured rock mass evolution prediction models, geological hazard prevention, and geological carbon sequestration optimization. Full article

Background/Objectives: Atypical femoral fracture (AFF) represents a diagnostic and therapeutic challenge, particularly in autoimmune disease patients receiving long-term bisphosphonate (BP) and glucocorticoid (GC) therapy. Although bilateral AFF is common, the radiographic evolution of asymptomatic incomplete lesions identified at the time of a complete fracture remains insufficiently defined. This study aimed to characterize the natural history and imaging biomarkers associated with progression in this biologically homogeneous high-risk population. Methods: Ten female autoimmune disease patients with complete AFF and asymptomatic incomplete contralateral lesions were retrospectively evaluated over a mean 59 months. Serial radiographs were assessed for cortical beaking, periosteal flaring, and transverse radiolucent lines. All patients discontinued BP therapy postoperatively; teriparatide was administered when tolerated. Results: Six lesions regressed, three remained stable, and one progressed—this progressing case being the only limb with a transverse radiolucent line at baseline. No patient developed symptoms or sustained a complete fracture on the contralateral side. Radiographic remodeling occurred independently of symptoms. BP discontinuation and, when tolerated, teriparatide appeared to contribute to lesion stabilization, although statistical significance was not achieved. Conclusions: In autoimmune patients with severe long-term BP and GC exposure, most asymptomatic incomplete AFF identified at the time of contralateral complete fracture remains stable or improves under conservative management. A transverse radiolucent line is the most decisive imaging biomarker predictive of progression and warrants intensified surveillance or consideration of prophylactic fixation. Larger cohorts are needed to refine risk stratification algorithms and optimize diagnostic and management strategies. Full article

Siliceous rocks of various colors and types are extensively developed within the Middle–Lower Ordovician carbonate along the Northwest Tarim Basin. Their genesis provides important insights into the evolution of basinal fluids and the associated diagenetic alterations of the carbonates. Based on petrographic, geochemical, fluid inclusion, and petrophysical analyses, this study investigates the origin of siliceous rocks within the Middle–Lower Ordovician carbonate formations (Penglaiba, Yingshan, and Dawangou formations) in the Kalpin area, Tarim Basin, and investigates the impact on hydrothermal reservoirs. The results reveal two distinct episodes of siliceous diagenetic fluids: The first during the Late Ordovician involved mixed hydrothermal fluids derived from deep magmatic–metamorphic sources, formation brines, and seawater. Characterized by high temperature and moderate salinity, it generated black chert dominated by cryptocrystalline to microcrystalline quartz through replacement processes. The second episode developed in the Middle–Late Devonian as a mixture of silicon-rich fluids from deep heat sources and basinal brines. In conditions of low temperature and high salinity, it generated gray-white siliceous rocks composed of micro- to fine crystalline quartz, spherulitic-fibrous chalcedony, and quartz cements via a combination of hydrothermal replacement and precipitation. A reservoir analysis reveals that the multi-layered black siliceous rocks possess significant reservoir potential amplified by the syndiagenetic tectonic fracturing. In contrast, the white siliceous rocks, despite superior petrophysical properties, are limited in scale as they predominantly infill late-stage fractures and vugs, mainly enhancing local flow conduits. Hydrothermal alteration in black siliceous rocks is more intense in dolostone host rocks than in limestone. Thus, thick (10–20 m), continuous black siliceous layers in dolostone and the surrounding medium-crystalline dolostone alteration zones, are promising exploration targets. This study elucidates the origins of Ordovician siliceous rocks and their implications for carbonate reservoir properties. The findings may offer valuable clues for deciphering the evolution and predicting the distribution of hydrothermal reservoirs, both within the basin and in other analogous regions worldwide. Full article

Machine learning has been widely applied to phase prediction and property evaluation in multi-principal element alloys. In this work, a data-driven machine learning framework is proposed to predict the ultimate tensile strength (UTS) and total elongation (TE) of Fe-Co-Cr-Ni-Mn-Al-Ti multi-principal element alloys (MPEAs), offering a cost-effective route for the design of new MPEAs. A dataset was compiled through an extensive literature survey, and six different machine learning models were benchmarked, from which XGBoost was ultimately selected as the optimal model. The feature set was constructed on the basis of theoretical considerations and experimental data reported in the literature, and SHAP analysis was employed to further elucidate the relative importance of individual features. By imposing constraints on the screened features, two alloys predicted to exhibit superior performance under different heat-treatment conditions were identified and fabricated for experimental validation. The experimental results confirmed the reliability of the model in predicting fracture strength, and the errors observed in ductility prediction were critically examined and discussed. Moreover, the strengthening mechanisms of the designed MPEAs were further explored in terms of microstructural characteristics and lattice distortion effects. The alloy design methodology developed in this study not only provides a theoretical basis for exploring unexplored compositional spaces and processing conditions in multi-principal element alloys, but also offers an effective tool for developing novel alloys that simultaneously achieve high strength and good ductility. Full article

Ceramic matrix composites (CMCs) exhibit excellent high-temperature strength, oxidation resistance, and fracture toughness, making them superior to traditional metals and single-phase ceramics in extreme environments such as aerospace, nuclear energy equipment, and high-temperature protection systems. The mechanical properties of CMCs directly influence the reliability and service life of structures; thus, accurately predicting their mechanical response and service behavior has become a core issue in current research. However, the multi-phase heterogeneity of CMCs leads to highly complex stress distribution and deformation behavior in traditional mechanical property testing, resulting in significant uncertainty in the measurement of key mechanical parameters such as strength and modulus. Additionally, the high manufacturing cost and limited experimental data further constrain material design and performance evaluation based on experimental data. Therefore, the development of effective numerical simulation and mechanical modeling methods is crucial. This paper provides an overview of the research hotspots and future directions in the field of CMCs numerical simulation and mechanical modeling through bibliometric analysis using the CiteSpace software. The analysis reveals that China, the United States, and France are the leading research contributors in this field, with 422, 157, and 71 publications and 6170, 3796, and 2268 citations, respectively. At the institutional level, Nanjing University of Aeronautics and Astronautics (166 publications; 1700 citations), Northwestern Polytechnical University (72; 1282), and the Centre National de la Recherche Scientifique (CNRS) (49; 1657) lead in publication volume and/or citation influence. Current research hotspots focus on finite element modeling, continuum damage mechanics, multiscale modeling, and simulations of high-temperature service behavior. In recent years, emerging research frontiers such as interface debonding mechanism modeling, acoustic emission monitoring and damage correlation, multiphysics coupling simulations, and machine learning-driven predictive modeling reflect the shift in CMCs research, from traditional experimental mechanics and analytical methods to intelligent and predictive modeling. Full article

A major danger that significantly raises the possibility of deep coal mining accidents is the delayed water influx from the bottom plate, which is brought on by the activation of tiny faults brought on by mining at the working face of the restricted aquifer. This study develops 17 numerical models utilizing FLAC3D simulation software 6.00.69 to clarify the activation and water inburst mechanisms of minor faults influenced by various parameters, incorporating fluid–solid coupling effects in coal seam mining. The developmental patterns of the stress field, displacement field, plastic zone, and seepage field of the floor rock layer were systematically examined in relation to four primary factors: aquifer water pressure, minor fault angle, fracture zone width, and the distance from the coal seam to the aquifer. The results of the study show that the upper and lower plates of the minor fault experience discontinuous deformation as a result of mining operations. The continuity of the rock layers below is broken by the higher plate’s deformation, which is significantly larger than that of the lower plate. The activation and water flow into small faults are influenced by many elements in diverse ways. Increasing the distance between the coal seam and the aquifer will make the water conduction pathway more resilient. This will reduce the amount of water that flows in. On the other hand, higher aquifer water pressure, a larger fracture zone, and a fault that is tilted will all help smaller faults become active and create channels for water to flow into. The gray relational analysis method was used to find out how sensitive something is. The sensitivities of each factor to water influence were ranked from high to low as follows: distance between the aquifer and coal seam (correlation coefficient 0.766), aquifer water pressure (0.756), width of the fracture zone (0.710), and angle of the minor fault (0.673). This study statistically elucidates the inherent mechanism of delayed water instillation in minor faults influenced by many circumstances, offering a theoretical foundation for the accurate prediction and targeted mitigation of mine water hazards. Full article

The construction of underground reservoirs in coal goafs is an innovative technology to alleviate the coal–water conflict in arid mining areas of northwest China. However, its widespread application is constrained by the challenge of accurately predicting water inflow, which fluctuates significantly due to the dynamic “expansion–closure” behavior of mining-induced fractures. This study focuses on the Shendong mining area, where repeated multi-seam mining occurs, and employs a coupled Finite Discrete Element Method (FDEM) and Computational Fluid Dynamics (CFD) numerical model, combined with in situ tests such as drilling fluid loss and groundwater level monitoring, to quantify the evolution of overburden fractures and their impact on reservoir water inflow during mining, 8 months post-mining, and after 7 years. The results demonstrate that the height of the water-conducting fracture zone decreased from 152 m during mining to 130 m after 7 years, while fracture openings in the key aquifer and aquitard were reduced by over 50%. This closure process caused a dramatic decline in water inflow from 78.3 m³/h to 2.6 m³/h—a reduction of 96.7%. The CFD-FDEM simulations showed a deviation of only 10.6% from field measurements, confirming fracture closure as the dominant mechanism driving inflow attenuation. This study reveals how fracture closure shifts water flow patterns from vertical to lateral recharge, providing a theoretical basis for optimizing the design and sustainable operation of underground reservoirs. Full article

The flexural behavior of reinforced rubberized concrete beams was assessed, and it was demonstrated that they exhibited a constant performance decline with an increase in rubber content. Numerical simulations are critically important in the study and engineering of concrete elements due to several key reasons, as follows: to allow engineers to anticipate the behavior of concrete components under diverse loads; to help elucidate intricate mechanisms such as crack initiation, propagation, and fracture processes; and to explore new materials, geometries, and reinforcement layouts without the need for extensive prototyping. This paper presents both experimental and numerical investigations on the flexural behavior of conventional and rubberized concrete reinforced beams. The parameters of the research included the percentage replacement of natural aggregates by rubber particles and the change in the longitudinal reinforcement ratio. The results showed an increase in the load-carrying capacity and a decrease in the midspan deflection with an increase in reinforcement ratio. Substituting natural aggregates with rubber particles resulted in a slight decrease in the load-carrying capacity but an increase in the midspan deflections. Numerical simulations using ATENA v5 software predicted the load-carrying capacity, failure mode, and cracking patterns of the reinforced concrete beams. Full article

Understanding particle transport in rough-walled fractures is essential for predicting flow behavior, clogging, and permeability evolution in natural and engineered subsurface systems. This study develops a fully coupled CFD–DEM framework to investigate how self-affine fractal roughness, represented by the Joint Roughness Coefficient (JRC), governs fluid–particle interactions across multiple scales. Nine fracture geometries with controlled roughness were generated using a fractal-based surface model, enabling systematic isolation of roughness effects. The results show that increasing JRC introduces a hierarchy of geometric perturbations that reorganize the flow field, amplify shear and velocity-gradient fluctuations, and enhance particle–wall interactions. Particle migration exhibits a nonlinear response to roughness due to the competing influences of disturbance amplification and the formation of preferential high-velocity pathways. Furthermore, roughness-controlled scaling relations are identified for mean particle velocity, residence time, and energy dissipation, revealing JRC as a fundamental parameter linking geometric complexity to transport efficiency. Based on these findings, a unified mechanistic framework is established that conceptualizes fractal roughness as a multiscale geometric forcing mechanism governing hydrodynamic heterogeneity, particle dynamics, and dissipative processes. This framework provides new physical insight into transport behavior in rough fractures and offers a scientific basis for improved prediction of clogging, proppant placement, and transmissivity evolution in subsurface engineering applications. Full article

This critical review explores the transformative impact of artificial intelligence (AI), particularly machine learning (ML) and computer vision (CV), on scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) for metallic microstructure analysis, spanning research from 2010 to 2025. It critically evaluates how AI techniques balance automation, accuracy, and scalability, analysing why certain methods (e.g., Vision Transformers for complex microstructures) excel in specific contexts and how trade-offs in data availability, computational resources, and interpretability shape their adoption. The review examines AI-driven techniques, including semantic segmentation, object detection, and instance segmentation, which automate the identification and characterisation of microstructural features, defects, and inclusions, achieving enhanced accuracy, efficiency, and reproducibility compared to traditional manual methods. It introduces the Microstructure Analysis Spectrum, a novel framework categorising techniques by task complexity and scalability, providing a new lens to understand AI’s role in materials science. The paper also evaluates AI’s role in chemical composition analysis and predictive modelling, facilitating rapid forecasts of mechanical properties such as hardness and fracture strain. Practical applications in steelmaking (e.g., automated inclusion characterisation) and case studies on high-entropy alloys and additively manufactured metals underscore AI’s benefits, including reduced analysis time and improved quality control. Extending prior reviews, this work incorporates recent advancements like Vision Transformers, 3D Convolutional Neural Networks (CNNs), and Generative Adversarial Networks (GANs). Key challenges—data scarcity, model interpretability, and computational demands—are critically analysed, with representative trade-offs from the literature highlighted (e.g., GANs can substantially augment effective dataset size through synthetic data generation, typically at the cost of significantly increased training time). Full article

To improve the computational efficiency of complex fatigue assessments, this study proposes a framework that integrates high-fidelity finite element analysis (FEA)with ensemble learning for evaluating the fatigue performance of weathering steel welded joints. First, a three-dimensional crack propagation model for cruciform fillet welds was developed on the ABAQUS-FRANC3D platform, with a validation error of less than 20%. Subsequently, a large-scale parametric analysis was conducted. The results indicate that as the stress amplitude increases from 67.5 MPa to 99 MPa, the fatigue life decreases to 40.29% of the baseline value. When the stress amplitude reaches 180 MPa, the fatigue life drops sharply to 14.28% of the baseline. Within the stress ratio range of 0.1 to 0.7, increasing the initial crack size from 0.075 mm to 0.5 mm reduces the fatigue life to between 85.78% and 86.48% of the baseline. Edge cracks, influenced by stress concentration, exhibit approximately 15.2% shorter fatigue life compared to central cracks, while the maximum variation in fatigue life due to crack geometry is only 10.25%. Second, an Extremely Randomized Trees surrogate model constructed based on the simulation data demonstrates excellent performance. Finally, by integrating this model with Paris’s law, the developed prediction framework achieves high consistency with numerical simulation results, with all predicted values falling within the two-standard-deviation interval. This data-driven approach can effectively replace computationally intensive finite element analysis, enabling efficient structural safety assessments. Full article

Based on a nonlinear effective stress coefficient calculation method, this study investigates the nonlinear stress sensitivity of permeability in deep shale gas reservoirs through high-temperature, high-pressure experiments on matrix, unpropped fracture, and propped fracture samples. Furthermore, the influence of different effective stress models on production performance in deep shale gas wells was investigated using the PETREL integrated fracturing production simulation module. Results reveal significant nonlinearity in the effective stress behavior of all media, with matrix samples showing much stronger permeability stress sensitivity than fracture samples. Numerical simulations revealed that horizontal well productivity under the nonlinear effective stress model was lower than predictions from the net stress model, providing critical theoretical and technical foundations for the large-scale and efficient development of deep marine shale gas reservoirs in the Sichuan Basin and emphasizing the importance of accurate stress models for production performance forecasting. Full article