Search Results (948)

Post-hemorrhagic hydrocephalus (PHH) is a serious neurological sequela of severe intraventricular hemorrhage (IVH) in premature infants and remains one of the leading indications for ventriculoperitoneal (VP) shunt placement. Evaluating possible VP shunt-related complications can be challenging because clinical manifestations are often nonspecific, neuroimaging may initially appear unchanged, and microbiological cultures may remain negative. We report the case of a 19-month-old male born at 28 weeks’ gestation who developed Grade IV germinal matrix/intraventricular hemorrhage with bilateral intraparenchymal extension, early periventricular cystic leukomalacia, and post-hemorrhagic communicating hydrocephalus requiring multiple cerebrospinal fluid diversion procedures culminating in long-term VP shunt dependence. His medical history was notable for recurrent neonatal meningitis, secondary epilepsy with previous episodes of status epilepticus, secondary adrenal insufficiency, and severe global developmental delay. He presented with fever, recurrent coffee-ground vomiting, abdominal distension, progressive lethargy, reduced responsiveness, and localized erythematous swelling over the cranial VP shunt reservoir, raising concern for possible shunt-related pathology. During hospitalization, he deteriorated with status epilepticus and respiratory failure, with clinical concern for increased intracranial pressure, requiring admission to the Pediatric Intensive Care Unit (PICU). Laboratory investigations demonstrated leukocytosis, elevated C-reactive protein, cerebrospinal fluid pleocytosis, markedly elevated CSF protein, and CSF glucose of 2.0 mmol/L, for which a paired serum glucose value was unavailable, while repeated blood, urine, wound, and CSF cultures remained negative. Initial computed tomography (CT) demonstrated no significant interval change in the chronic hydrocephalus despite progressive neurological deterioration; however, serial neuroimaging later revealed progressive bilateral extra-axial fluid collections with radiological features suggestive of an evolving subacute subdural hemorrhage. The patient was managed with empirical broad-spectrum intravenous antibiotics, aggressive seizure control, stress-dose corticosteroids, respiratory support, and continuous multidisciplinary monitoring. His neurological and respiratory status subsequently improved, and he returned to his pre-admission neurological baseline before discharge with planned further evaluation at a tertiary pediatric neurosurgical center. This case highlights the diagnostic uncertainty surrounding acute neurological deterioration in a child with shunt-dependent PHH. VP shunt-related infection or malfunction remained important but unconfirmed diagnostic considerations, alongside competing or potentially overlapping contributors including status epilepticus, evolving extra-axial collections, respiratory infection, and endocrine or metabolic decompensation. No single etiology was definitively established. The case emphasizes the importance of serial neurological assessment, consideration of alternative diagnoses, repeat neuroimaging, and multidisciplinary evaluation when initial investigations do not establish the cause of deterioration. Full article

If a CO₂ reservoir or transmission pipeline were to leak, both the surrounding ecology and maritime traffic safety could be put at risk. To better understand and prepare for this risk, multiphase Computational Fluid Dynamics (CFD) models were built in ANSYS Fluent to capture the behavior of a leak once it enters the water. A 3D Eulerian–Eulerian model was used for validation, while a simplified 2D model was applied to simulate conditions at a 50-m depth. The models integrate bubble dynamics, gas holdup, CO₂ dissolution, dissolved species transport, and seawater acidification into a unified CFD framework. Mass transfer was calculated using the Hughmark correlation, and local seawater temperature and salinity were factored in to determine dissociation behavior and the relevant Henry’s Law constant. To confirm the 3D model’s accuracy, results were checked against two experimental datasets: the QICS field study and the Hauser Tank experiments. The team also modeled a hypothetical release scenario at the High Island 10L site and compared the results with earlier published work. The results show that at a depth of 50 m, the surrounding water column can completely absorb a CO₂ release at a rate of 35 kg/s, since the gas dissolves into the seawater as it rises toward the surface. Beyond confirming this mitigation capacity, the simulations shed light on how a leak would actually unfold in the environment, including the shape and movement of the rising bubble plume, how much CO₂ dissolves along the way, and the resulting shifts in seawater pH and pCO₂. Together, this provides a practical framework for assessing how CO₂ leaks could affect marine environments in the Gulf of Mexico. Full article

We present ESNformer, a hybrid architecture that couples an Echo State Network (ESN) reservoir with a Transformer encoder for classification of structured, multi-indicator assessment data: a fixed-order vector of complementary indicators per assessment instance rather than a repeated-measures time series. The reservoir acts as a fixed nonlinear feature map over the indicator vector, while self-attention, made position-aware over the fixed column order, learns how each indicator’s evidence contributes to the final decision, so the two components, together, capture local, indicator-level detail and global, cross-indicator interactions within a single, end-to-end trainable model. Interpretability is treated as a first-class design requirement rather than an afterthought: the architecture is paired with an explainability layer combining SHAP feature attribution (reported both globally and per class), the model’s own attention weights, a deletion/insertion faithfulness test that quantitatively verifies which inputs the model actually relies on, and counterfactual maps that translate a prediction into an actionable, inspectable recommendation. We evaluate the architecture on a concrete case study, classifying Braille-literacy instructional recommendations from 15 pedagogical indicators grouped into three categories (Mangold’s, ABKL, and Progresar), using a benchmark of 900 real assessment instances (630 used, together with a class-conditional augmentation procedure, to build a 2100-instance training set) with validation and test partitions (135 instances each) kept exclusively real. On this benchmark, the tuned model reached 85.33% accuracy, 85.90% macro-precision, 85.33% macro-recall, an F1 score of 85.25%, and an AUC of 0.95 on the real test set. SHAP attribution, attention weights, and the faithfulness test converge on the same two dominant indicators (response time and error count): removing them alone collapses accuracy to chance, while retaining only them recovers most of the model’s accuracy. We report this transparently alongside a comparison against ESN-only, Transformer-only, and tabular baselines (logistic regression, decision tree, random forest, XGBoost, and an MLP) on the same data and discuss what the hybrid architecture and its explainability pipeline add beyond what the two dominant indicators already explain and how the approach generalizes to other tabular and mixed-granularity assessment settings that require both predictive accuracy and a verifiable account of what drove each decision. Full article

Geothermal energy and underground thermal storage (UTES) are vital to the low-carbon energy transition, yet their optimization is bottlenecked by multi-scale heterogeneity, coupled thermal–hydraulic–mechanical–chemical (THMC) processes, and the high computational cost of full-physics simulations. This review systematically evaluates machine learning (ML) as a foundational paradigm for overcoming these computational and scale-bridging challenges. We categorize current advances into three key functional roles. First, data-driven upscaling directly maps pore-scale features to macro-scale effective properties, replacing traditional empirical homogenization. Second, deep surrogate models mimic high-fidelity THMC simulations at a fraction of the computational cost, enabling real-time prediction and uncertainty quantification. Third, physics-informed digital twins integrate real-time sensor streams with cloud architectures for dynamic reservoir management. Furthermore, we address the generalization limits of purely data-driven approaches, highlighting physics-informed machine learning (PIML) and hybrid architectures that embed conservation laws as strict constraints. Finally, we outline future pathways toward multimodal data fusion and edge-cloud deployment, marking a shift from static offline modeling to dynamic, physics-safeguarded real-time reservoir optimization. Full article

To address the challenge of efficiently blending low-mutual-solubility gas–liquid two-phase systems, a composite structure comprising a Venturi and a static mixer was designed, and its flow field characteristics were analyzed using computational fluid dynamics (CFD) simulations. The results indicate that positioning the static mixer at the exit of the Venturi diffusion section yields optimal performance. This configuration prevents disruption of the jet premix flow field and facilitates the uniform dispersion of gas–liquid mixtures throughout the entire domain via six sets of SK-type single-spiral static mixer (SK) units following the initial blending. The composite structure exhibits a three-tier synergistic mechanism characterized by “suction–premix–mixing intensification”: the negative pressure zone within the throat tube induces suction of the gas phase, the diffusion section converts pressure energy to enhance shearing and crushing, and the static mixing section disrupts the axial jet through cutting and swirling effects, thereby generating secondary vortices. This process ultimately achieves uniform dispersion of gas and liquid across the entire domain. The structure’s lack of moving parts addresses the issues of low efficiency and unstable flow fields associated with traditional devices. This design facilitates enhanced crude oil recovery and low-pressure reservoir gas injection drilling. Full article

Tight sandstone reservoir effectiveness is governed not by pore volume alone, but by the storage and flow contributions of different pore-throat scales, their structural complexity, and their three-dimensional connectivity. This study investigates tight sandstones of the Benxi Formation deposited in a marine–continental transitional mixed siliciclastic–carbonate setting in the Gaoqiao area, southern Ordos Basin. Petrophysical measurements, red-epoxy-impregnated thin-section petrography, mercury intrusion capillary pressure (MICP), segment-specific fractal analysis of functionally defined pore-throat regimes, X-ray micro-computed tomography (micro-CT), and pore-network modeling (PNM) were integrated. The MICP responses define three pore-throat structure types and two data-derived functional boundaries at 0.708 and 0.141 μm, which separate large-pore-throat-dominated, transitional pore-throat, and fine-throat-limited intervals. Using these nominal boundaries, Type I is strongly dominated by the large-pore-throat interval, which accounts for 88.7% of total mercury intrusion, whereas Type II exhibits a mixed large-to-transitional response, and Type III is characterized by negligible large-pore-throat intrusion and pronounced fine-throat restriction. Perturbing both functional boundaries by ±5% and ±10% does not alter these principal functional distinctions, although samples close to the second boundary exhibit the expected local transitional sensitivity. Among the three segment-specific fractal parameters, the fine-throat fractal dimension, D_B, shows the strongest association with median capillary pressure (r = 0.834, p < 0.001) and remains significantly related to displacement pressure, median pore-throat radius, and permeability, whereas D_T shows no significant linear correlation with the tested petrophysical and MICP parameters. The fractions of the largest connected pore cluster in representative Type I–III samples are 90.26%, 72.56%, and 64.65%, while their PNM permeabilities decrease successively from 64.32 mD to 0.850 and 0.121 mD. Together, these results indicate that, for the investigated samples, reservoir effectiveness reflects the combined influence of pore-throat size configuration, segment-specific structural complexity, and three-dimensional network connectivity. Full article

To address the challenges of complex multi-objective trade-offs, tightly coupled physical constraints in reservoir dam safety dispatching, and fulfill the significant cognitive gaps in human–machine interaction, a framework with four deep cognitive layers and a physical computation layer is proposed which integrates large language models (LLMs) with multi-agent collaboration. The framework stratifies cognitive intelligence into interface translation, strategic cognition, tactical reasoning, and operational understanding layers; performs computation in the physical computation layer; and achieves deep coupling among agents in different layers through the Blackboard information sharing mechanism. The physical computation layer consists of the gate-opening discharge, water-level storage capacity, runoff and inflow, downstream risk calculation agents and a Pareto multi-objective optimizer to realize non-dominated sorting of multi-dimensional objectives encompassing dam safety, ecological loss, downstream risk, and operational complexity. Illustrative case analysis indicates that this framework can effectively parse user requirements expressed in natural language, generate dispatching schemes conforming to physical constraints, achieve error control and quantify the downstream risk. This research provides a scalable framework for the implementation of intelligent reservoir dispatching and can enhance the intelligence of digital twins of river basins. Full article

Atrial cardiomyopathy (ACM) is defined as any complex of structural, architectural, functional, electrophysiological, and molecular changes affecting the atria that may result in clinically significant health consequences. ACM can be caused by a variety of factors, including age-related changes, valvular or vascular disease, genetic diseases, congestive heart failure, metabolic diseases, cardiovascular disease (CVD) risk factors such as arterial hypertension (AH) or obesity, obstructive sleep apnea, and other infectious or noninfectious diseases predisposing to chronic inflammation. The diagnosis of ACM relies on several modalities, including electrocardiography, echocardiography, cardiac magnetic resonance imaging (MRI), computed tomography (CT), electroanatomical mapping (EAM), genetic studies, and biomarkers, which can detect and characterize structural, mechanical, and electrical atrial dysfunction. These changes often include structural atrial remodeling (fibrosis), abnormal structure of the atrial wall and its components, and contractile and electrical dysfunctions. When assessing aspects of ACM, structural changes in the atria such as left atrium (LA) size and fibrosis; LA architectural changes such as the expression of remodeling; changes in LA mechanics such as echocardiographic stress indices; changes in reservoir function and changes in contraction; biological factors determining changes in biomarkers; possible genetic predispositions and higher expression of certain genes encoding certain proteins; and arrhythmogenic factors associated with a higher risk of atrial fibrillation (AF) and stroke and a worse short- and long-term prognosis are very important. When considering the challenges of diagnosing ACM, it should be noted that without standardized diagnostics, most ACM diagnostic situations remain primarily research tools rather than practical clinical diagnostic methods. This review critically evaluates the evidence and translational gaps in the diagnosis of ACM, synthesizing the emerging role of advanced diagnostics and their clinical and prognostic implications as a key future tool for individual risk stratification. Full article

Migrant resettlement communities in large-scale development regions face persistent pressures from environmental change, livelihood disruption, and social restructuring. Evaluating resilience in such communities requires a framework that captures both external stressors and internal adaptive capacity. This study asks whether differences in resilience between resettlement communities are driven more by differences in external pressure exposure or by differences in internal development conditions. To address this question it applies a Pressure–State–Response (PSR) framework to assess community resilience in the Three Gorges Reservoir Area (TGRA) of China. A four-level indicator system of 29 variables was developed across pressure, state, and response dimensions. Weights were determined by combining the Analytic Hierarchy Process with the entropy method; standardisation and entropy weights were computed across all 25 surveyed communities. Survey data statistical records, and remote sensing information were collected from 25 migrant resettlement communities. Three of these communities—CT, SQ, and GGB—were then selected by purposive maximum-variation sampling for detailed comparison. Results show that overall resilience ranked CT (0.646) > GGB (0.600) > SQ (0.483), with state resilience contributing the largest share to composite scores in all three communities. Obstacle-factor analysis revealed distinct constraint profiles: CT was primarily limited by ecological pressure, SQ by weak livelihood development, and GGB by limited industrial diversity and incomplete social integration. These findings indicate that resilience in resettlement communities depends more on internal development quality than on exposure levels alone; a Monte Carlo analysis confirmed that this ordering held in 99.9% of simulated draws. Differentiated governance strategies are needed rather than uniform approaches. The PSR-based framework and obstacle-factor diagnosis provide a practical tool for assessing and improving resilience in communities shaped by large-scale infrastructure development. Full article

Marine macroalgae represent abundant, renewable reservoirs of structurally unique bioactive compounds, such as sulfated polysaccharides, phlorotannins, and carotenoids, with immense potential for sustainable functional foods. However, their industrial exploitation is severely bottlenecked by complex, repeat-rich genomes, recalcitrant genetic transformation tools, and environmental cultivation variability. Synthesizing evidence from 180 high-quality studies spanning from 1961 to 2026, this review provides a comprehensive synthesis of how artificial intelligence (AI) and synthetic biology may contribute to overcoming these challenges. We highlight key advances across the bioengineering pipeline, including the application of metabolic engineering strategies for enhancing valuable compound production in engineered algal systems. For example, a CrtYB-based metabolic engineering approach achieved β-carotene accumulation of 22.8 mg/g in the microalga Chlamydomonas reinhardtii, providing important insights for future metabolic engineering of marine macroalgae. In addition, AI-assisted approaches show promising potential for enzyme discovery, metabolic pathway prediction, and multi-omics-guided optimization of bioactive compound production. We further discuss critical downstream challenges, including the low gastrointestinal absorption (~14%) and extensive metabolic transformation of seaweed-derived phenolic compounds, as well as the potential application of AI-integrated physiological modeling for improving bioavailability prediction and safety assessment. This review provides a pioneering, data-driven synthesis of how the convergence of AI and synthetic biology is overcoming these roadblocks. Moving beyond generic descriptions, we highlight key empirical milestones across the bioengineering pipeline, including multi-fold yield enhancements in target pigments (up to 22.8 mg/g) and the AI-driven discovery of novel polysaccharide-degrading enzymes. Furthermore, we confront critical downstream challenges, specifically addressing the characteristically low (~14%) gastrointestinal absorption bottleneck and extensive metabolic biotransformation of seaweed phenolics. We demonstrate that integrating digital twins with reinforcement learning-driven physiologically based pharmacokinetic (PB-PK) modeling can compress the R&D cycles of these seaweed functional ingredients by over 60%. Unlike previous reviews that treat these technologies as independent entities, this article proposes a macroalgae-focused approach that delivers a unique, macroalgae-specific computational and experimental framework, providing a future roadmap toward intelligent smart marine biotechnology and sustainable development to drive the global blue bioeconomy. Full article

Reservoir stimulation through fracturing is essential for the commercial development of unconventional oil and gas resources. Supercritical CO₂ (scCO₂) combines liquid-like density with gas-like viscosity and compressibility, enabling efficient conversion of stored energy into shock waves and jet impacts. This study introduces an innovative scCO₂ shock fracturing technique, in which a downhole pressure-control valve rapidly releases compressed scCO₂ to generate transient shock pressures that induce rock fracture initiation and propagation. A series of scCO₂ shock fracturing experiments were conducted on sandstone and shale to evaluate the influence of different impact loads on both macroscopic and microscopic damage. Rock damage evolution was characterized using computed tomography (CT), nuclear magnetic resonance (NMR), mercury intrusion porosimetry (MIP), and quantitative analysis of fracture surface morphology. The results showed that increasing shock pressure enhanced fracture surface roughness, shear slip, and particle spalling in sandstone, producing rough tensile–shear fracture surfaces with a potential self-supporting tendency. NMR results indicated that sandstone mainly exhibited a single-peak T₂ response, and scCO₂ shock loading primarily affected pores and pore-fracture spaces larger than 0.08 µm. In contrast, shale showed a broader and more heterogeneous pore-fracture response, with preferential enlargement and connection of large pore-fracture spaces. The NMR-MIP-calibrated equivalent pore-fracture diameter distribution showed that scCO₂ shock fracturing mainly promoted pore-fracture spaces larger than 0.2 μm in shale; at 40 MPa, the volume of this pore-fracture range increased by approximately 6.75 times. However, the characteristic equivalent pore-fracture diameter decreased at 45 MPa, which is attributed to severe specimen fragmentation, fragment displacement, scCO₂ escape, and energy dissipation. These findings suggest that scCO₂ shock fracturing is a promising stimulation approach for enhancing macroscopic fracturing and microscopic pore-fracture reconstruction in unconventional reservoirs. Full article

Geomechanical properties of rock are essential components in designing hydraulic fracturing procedures. Although ultrasonic measurement methods are usually utilized to obtain static geomechanical properties of rocks in the laboratory, they are limited to borehole locations. To obtain spatial distribution of these properties, a 3D prestack seismic inversion process is employed to derive them dynamically. However, 3D prestack seismic datasets are less readily available compared to 3D poststack seismic. We present a methodology that integrates 3D poststack seismic and wireline log data using a machine learning workflow to compute the dynamic geomechanical properties, namely Young’s modulus (E), Mu-Rho (MR), and brittleness (BRI) volumes, and generate crossplots to characterize the Salt Creek carbonate reservoir in the Midland Basin, Kent County, Texas. Our results show that: (1) Based on the comparison of seismically (dynamically) derived E and BRI maps with litho-facies maps, the zones with the highest porosity (oolites) are characterized by low E and low BRI. (2) Each of these properties is linearly related to the photoelectric factor (PEF) log, which can indicate porosity and calcite richness within a mixed carbonate–siliciclastic system. (3) In a mixed carbonate and siliciclastic system, geomechanical properties, especially E, can be used to identify rigid rock layers within the reservoir and deduce possible lithologies. Finally, when prestack seismic data are unavailable, our workflow offers a quick and inexpensive method to generate dynamic geomechanical property maps to characterize hydrocarbon reservoirs using poststack seismic data and well logs. Full article

The complexity of micrometer-scale pore-throat structures in sandstone reservoirs strongly controls the occurrence state and mobilization degree of residual oil after water-flooding. To clarify the differences in residual oil occurrence between pure oil-zone and transition-zone reservoirs and their microscopic controlling mechanisms, sandstone samples were collected from the SII oil layer group, which belongs to the Upper Cretaceous Yaojia Formation, in the Nanqi area of the Daqing Oilfield, northern Songliao Basin, NE China, and were investigated. Mercury intrusion capillary pressure (MICP), two-dimensional nuclear magnetic resonance (2D NMR), laser scanning confocal microscopy (LSCM), micro-computed tomography (micro-CT), X-ray diffraction (XRD), wettability measurement and fractal analysis were integrated to systematically characterize the pore-throat architecture, mineral composition, seepage capacity, and residual oil occurrence of the two reservoir types. The results show that the pore-throat radius distributions are mainly unimodal. In the pure oil-zone samples, the pore-throat distribution is highly consistent with the corresponding permeability contribution curve, whereas evident deviations occur in some transition-zone samples. Large and medium pore throats exert the most significant control on seepage capacity, and the difference in fractal characteristics is mainly reflected by D1, the fractal dimension of large pore throats. The transition-zone reservoirs generally exhibit moderate to strong water-wet characteristics. Owing to the development of fine pore throats and strong capillary forces, water is prone to retention within pore-throat spaces, resulting in pronounced water-blocking and Jamin effects. After water-flooding, the pure oil-zone reservoirs exhibit lower residual oil saturation, with residual oil occurring mainly in a bound state; in contrast, the transition-zone reservoirs show higher residual oil saturation and relatively high proportions of free and semi-bound residual oil. Mineral composition further modifies pore-throat complexity and residual oil occurrence. D1 is negatively correlated with feldspar content, indicating that increased feldspar content helps improve the pore-throat structure, but positively correlated with clay mineral content, suggesting that clay minerals enhance structural complexity. In the transition-zone reservoirs, kaolinite and illite–smectite mixed-layer minerals are relatively well developed. Their velocity-sensitive and water-sensitive effects readily induce pore-throat blockage and increased flow resistance, which are important causes of residual oil enrichment and difficult oil mobilization in the transition zone. Full article

Multi-trace amplitude variation with angle inversion (MAVAI) is a vital tool for estimating the physical parameters of subsurface media, and it plays an important role in oil and gas exploration. However, the existing MAVAI method relies on the Kronecker product to construct an extremely large-scale inverse problem, and its computational inefficiency limits its widespread application. Furthermore, regarding regularization constraints, the existing MAVAI method only considers the smoothness of the inversion parameters, which leads to ambiguous formation boundaries and hinders accurate identification for complex reservoirs. To address these issues, a compressed MAVAI method with elastic half-norm regularization is proposed. Specifically, we first developed a compressed MAVAI (CMAVAI) framework that uses compressed measurements of seismic data and reference models in a sparse domain to construct the CMAVAI objective function, thereby reducing the scale of the inversion problem and improving inversion efficiency. Subsequently, the elastic half-norm is introduced into the CMAVAI framework as a regularization constraint for reservoir parameter estimation. Since the elastic half-norm can simultaneously characterize both the smoothness and blocky features of the subsurface medium, it effectively improves inversion accuracy compared to the MAVAI method. Finally, the performance of the proposed method is evaluated using a theoretical model and field data. The results demonstrate that, compared with the traditional MAVAI algorithm, the CMAVAI framework can effectively improve inversion efficiency while maintaining inversion accuracy. Moreover, the CMAVAI method regularized by the elastic half-norm can improve the accuracy of inversion parameters while retaining the high prediction efficiency of the CMAVAI framework. Full article

Direct core-scale evidence remains insufficient for evaluating hydraulic fracture–natural fracture coupling and stimulation effectiveness in tight sandstone oil reservoirs. In this study, post-fracturing full-diameter cores from the Chang 8₁ tight oil reservoir in the Xi 119 well block, Xifeng Oilfield, Ordos Basin, were investigated using core observation, computed tomography (CT) scanning, fracture-source evidence and three-dimensional fracture-network reconstruction. A total of 87.56 m of core from 11 core runs was scanned at a voxel size of 50.62 μm. Natural fractures, hydraulic fractures and engineering-induced fractures were identified and distinguished based on fracture-surface features, CT expression, spatial continuity, proppant/tracer evidence and their relationship with bedding and lithological boundaries. The results show that lithological structure exerts a first-order control on hydraulic-fracture surface morphology. Massive sandstone tends to generate straight and continuous high-conductivity main fractures, argillaceous laminated sandstone promotes bedding-controlled discontinuous fractures with limited connectivity, and cross-bedded sandstone favors fracture diversion, branching and natural-fracture activation. Based on fracture assemblage, spatial connectivity and seepage behavior, three hydraulic fracture–natural fracture coupling types were classified: single hydraulic-fracture type, single main fracture–diverted fracture–natural fracture type, and dual main fractures–diverted fractures–natural fractures type. Their equivalent permeability increases stepwise from 155 mD to 345 mD and 586 mD, respectively, indicating a positive relationship between fracture-network complexity and seepage capacity. A CT-derived stimulation-effect evaluation framework was further established by integrating pore–fracture structural modification, fracture volume increase, aperture improvement and seepage-capacity enhancement. The dual main fractures–diverted fractures–natural fractures type shows the strongest stimulation response, with the largest reduction in small-aperture pore/fracture proportion, the greatest lamina-fracture aperture enlargement and the most significant permeability improvement. These results provide direct core-scale evidence for understanding fracture-network formation in continental tight sandstone reservoirs and support more targeted hydraulic-fracturing design and stimulation-effect evaluation. Full article