Search Results (692)

The surface of the Great Wall harbors a large number of non-vascular plants dominated by cyanobacteria, lichens and mosses as well as microorganisms, and form biocrusts by cementing with the soils and greatly alters the pore structure of the soil and the ecohydrological processes associated with the soil pore space, and thus influences the soil resistance to erosion. However, the microscopic role of the biocrusts in influencing the pore structure of the surface of the Great Wall is not clear. This study chose the Warring States Qin Great Wall in Weiyuan, Gansu Province, China, as research site to quantify thepore structure characteristics of the three-dimensional of bare soil, cyanobacterial-lichen crusts, and moss crusts at the depth of 0–50 mm, by using optical microscopy, scanning electron microscopy, and X-ray computed tomography and image analysis, and the precipitation infiltration process. The results showed that the moss crust layer was dominated by large pores with long extension and good connectivity, which provided preferential seepage channels for precipitation infiltration, while the connectivity between the cyanobacterial-lichen crust voids was poor; The porosity of the cyanobacterial-lichen crust and the moss crust was 500% and 903.27% higher than that of the bare soil, respectively. The porosity of the subsurface layer of cyanobacterial-lichen crust and moss crust was significantly lower than that of the biocrusts layer by 92.54% and 97.96%, respectively, and the porosity of the moss crust was significantly higher than that of the cyanobacterial-lichen crust in the same layer; Cyanobacterial-lichen crusts increased the degree of anisotropy, mean tortuosity, moss crust reduced the degree of anisotropy, mean tortuosity. Biocrusts increased the fractal dimension and Euler number of pores. Compared with bare soil, moss crust and cyanobacterial-lichen crust increased the isolated porosity by 2555% and 4085%, respectively; Biocrusts increased the complexity of the pore network models; The initial infiltration rate, stable infiltration rate, average infiltration rate, and the total amount of infiltration of moss crusted soil was 2.26 and 3.12 times, 1.07 and 1.63 times, respectively, higher than that of the cyanobacterial-lichen crusts and the bare soil, by 1.53 and 2.33 times, and 1.13 and 2.08 times, respectively; CT porosity and clay content are significantly positively correlated with initial soil infiltration rate (|r| ≥ 0.85), while soil type and organic matter content are negatively correlated with initial soil infiltration rate. The soil type and bulk density are directly positively and negatively correlated with CT porosity, respectively (|r| ≥ 0.52). There is a significant negative correlation between soil clay content and porosity (|r| = 0.15, p < 0.001). Biocrusts alter the erosion resistance of rammed earth walls by affecting the soil microstructure of the earth’s great wall, altering precipitation infiltration, and promoting vascular plant colonisation, which in turn alters the erosion resistance of the wall. The research results have important reference for the development of disposal plans for biocrusts on the surface of archaeological sites. Full article

Large soil pores critically influence water and solute transport in soils. The presence of preferential flow paths created by soil macropores can profoundly impact water quality, underscoring the necessity of accurately assessing the characteristics of these macropores. However, it remains unclear whether variations in macropore structure exist between different altitudes and positions of terraced paddy fields. The primary objective of this research was to utilize X-ray computed tomography (CT) and image analysis techniques to characterize the soil pore structure at both the inner field and ridge positions across different altitude levels (high, medium, and low altitude) within terraced paddy fields. The results indicate that there are significant differences in the distribution of large soil pores at different altitudes, with large pores concentrated in the surface layer (0–10 cm) in low-altitude areas, while in high-altitude areas, the distribution of large pores is more uniform. Additionally, as altitude increases, the porosity of large pores shows an increasing trend. The three-dimensional equivalent diameter and large pore volume are primarily characterized by large pores ranging from 1 to 2 mm and 0 to 5 mm³, respectively, with their morphology predominantly appearing spherical or ellipsoidal. The connectivity of large pores in the surface layer of paddy soil is stronger than that in the bunds. However, this connectivity gradually weakens with increasing soil depth. The findings from this study provide valuable quantitative insights into the unique characteristics of soil macropores that vary according to the altitude and position in terraced paddy fields. Moreover, this study emphasizes the necessity for future research that encompasses a broader range of soil types, altitudes, and terraced paddy locations to validate and further explore the identified relationships between altitude and macropore characteristics. Full article

Tissue-engineered scaffolds, particularly composite scaffolds composed of polymers combined with ceramics, bioactive glasses, or nanomaterials, play a vital role in regenerative medicine by providing structural and biological support for tissue repair. As scaffold designs grow increasingly complex, the need for non-invasive imaging modalities capable of monitoring scaffold integration, degradation, and tissue regeneration in real-time has become critical. This review summarizes current non-invasive imaging techniques used to evaluate tissue-engineered constructs, including optical methods such as near-infrared fluorescence imaging (NIR), optical coherence tomography (OCT), and photoacoustic imaging (PAI); magnetic resonance imaging (MRI); X-ray-based approaches like computed tomography (CT); and ultrasound-based modalities. It discusses the unique advantages and limitations of each modality. Finally, the review identifies major challenges—including limited imaging depth, resolution trade-offs, and regulatory hurdles—and proposes future directions to enhance translational readiness and clinical adoption of imaging-guided tissue engineering (TE). Emerging prospects such as multimodal platforms and artificial intelligence (AI) assisted image analysis hold promise for improving precision, scalability, and clinical relevance in scaffold monitoring. Full article

Enhanced Geothermal Systems (EGS) require thermochemically stable proppant materials capable of sustaining fracture conductivity under harsh subsurface conditions. This study systematically investigates the response of commercial proppants to coupled thermo-hydro-chemical (THC) effects, focusing on chemical stability and microstructural evolution. Four proppant types were evaluated: an ultra-low-density ceramic (ULD), a resin-coated sand (RCS), and two quartz-based silica sands. Experiments were conducted under simulated EGS conditions at 130 °C with daily thermal cycling over a 25-day period, using diluted site-specific Utah FORGE geothermal fluids. Static batch reactions were followed by comprehensive multi-modal characterization, including scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS), X-ray diffraction (XRD), and micro-computed tomography (micro-CT). Proppants were tested in both granular and powdered forms to evaluate surface area effects and potential long-term reactivity. Results indicate that ULD proppants experienced notable resin degradation and secondary mineral precipitation within internal pore networks, evidenced by a 30.4% reduction in intragranular porosity (from CT analysis) and diminished amorphous peaks in the XRD spectra. RCS proppants exhibited a significant loss of surface carbon content from 72.98% to 53.05%, consistent with resin breakdown observed via SEM imaging. While the quartz-based sand proppants remained morphologically intact at the macro-scale, SEM-EDS revealed localized surface alteration and mineral precipitation. The brown sand proppant, in particular, showed the most extensive surface precipitation, with a 15.2% increase in newly detected mineral phases. These findings advance understanding of proppant–fluid interactions under low-temperature EGS conditions and underscore the importance of selecting proppants based on thermo-chemical compatibility. The results also highlight the need for continued development of chemically resilient proppant formulations tailored for long-term geothermal applications. Full article

The rapid spread of COVID-19 increased the need for speedy diagnostic tools, which led scientists to conduct extensive research on deep learning (DL) applications that use chest imaging, such as chest X-ray (CXR) and computed tomography (CT). This review examines the development and performance of DL architectures, notably convolutional neural networks (CNNs) and emerging vision transformers (ViTs), in identifying COVID-19-related lung abnormalities. Individual ResNet architectures, along with CNN models, demonstrate strong diagnostic performance through the transfer protocol; however, ViTs provide better performance, with improved readability and reduced data requirements. Multimodal diagnostic systems now incorporate alternative methods, in addition to imaging, which use lung ultrasounds, clinical data, and cough sound evaluation. Information fusion techniques, which operate at the data, feature, and decision levels, enhance diagnostic performance. However, progress in COVID-19 detection is hindered by ongoing issues stemming from restricted and non-uniform datasets, as well as domain differences in image standards and complications with both diagnostic overfitting and poor generalization capabilities. Recent developments in COVID-19 diagnosis involve constructing expansive multi-noise information sets while creating clinical process-oriented AI algorithms and implementing distributed learning protocols for securing information security and system stability. While deep learning-based COVID-19 detection systems show strong potential for clinical application, broader validation, regulatory approvals, and continuous adaptation remain essential for their successful deployment and for preparing future pandemic response strategies. Full article

This study presents a comprehensive methodology for the detection and characterization of fractures in geological samples using X-ray computed tomography (CT). By combining convolution-based image processing techniques with advanced neural network-based segmentation, the proposed approach achieves high precision in identifying complex fracture networks. The method was applied to a marly limestone sample from the Maiolica Formation, part of the Umbria–Marche stratigraphic succession (Northern Apennines, Italy), a geological context where fractures often vary in size and contrast and are frequently filled with minerals such as calcite or clays, making their detection challenging. A critical part of the work involved addressing multiple sources of uncertainty that can impact fracture identification and measurement. These included the inherent spatial resolution limit of the CT system (voxel size of 70.69 μm), low contrast between fractures and the surrounding matrix, artifacts introduced by the tomographic reconstruction process (specifically the Radon transform), and noise from both the imaging system and environmental factors. To mitigate these challenges, we employed a series of preprocessing steps such as Gaussian and median filtering to enhance image quality and reduce noise, scanning from multiple angles to improve data redundancy, and intensity normalization to compensate for shading artifacts. The neural network segmentation demonstrated superior capability in distinguishing fractures filled with various materials from the host rock, overcoming the limitations observed in traditional convolution-based methods. Overall, this integrated workflow significantly improves the reliability and accuracy of fracture quantification in CT data, providing a robust and reproducible framework for the analysis of discontinuities in heterogeneous and complex geological materials. Full article

MgO–C refractory materials were developed by incorporating different ratios of alumina/titania nano-additives which were synthesized chemically. Their physical and mechanical properties, oxidation resistance, slag wettability, bulk density, apparent porosity, cold crushing strength, oxidation index, and closed porosity were tested, evaluated, and compared using conventional techniques as well as X-ray micro-computed tomography (µCT). This investigation indicated a slight degradation of physical properties and mechanical strengthening which was stronger for samples with increased alumina content. Oxidation and corrosion extent were tested both with X-ray tomography and conventional methods. The first method allowed for the calculation of the oxidation index, the detection of closed porosity, and an improved analysis of the internal corrosion, avoiding the sectioning of the materials. This result confirms the supremacy of the first technique. On the contrary, although conventional methods such as the Archimedes procedure cannot detect close porosity, they provide more accurate measurements of the physical properties of refractories. This study shows that conventional methods exhibit superiority in investigations of the pore structures of refractories for pore sizes in the range 1–2 μm, while the use of the μCT system is limited for pore sizes equal to or larger than 20 μm. Full article

Recent studies primarily focus on how the fiber content and curing age influence the pore structure and strength of concrete. However, The interfacial bonding mechanism in basalt-fiber-reinforced concrete hydration remains unclear. The lack of a long-term performance-prediction model and insufficient research on multi-field coupling effects form key knowledge gaps, hindering the systematic optimal design and wider engineering applications of such materials. By integrating X-ray computed tomography (CT) with the watershed algorithm, this study proposes an innovative gray scale threshold method for pore quantification, enabling a quantitative analysis of pore structure evolution and its correlation with mechanical properties in basalt-fiber-reinforced concrete (BFRC) and normal concrete (NC). The results show the following: (1) Mechanical Enhancement: the incorporation of 0.2% basalt fiber by volume demonstrates significant enhancement in the mechanical performance index. At 28 days, BFRC exhibits compressive and splitting tensile strengths of 50.78 MPa and 4.07 MPa, surpassing NC by 19.88% and 43.3%, respectively. The early strength reduction in BFRC (13.13 MPa vs. 22.81 MPa for NC at 3 days) is attributed to fiber-induced interference through physical obstruction of cement particle hydration pathways, which diminishes as hydration progresses. (2) Porosity Reduction: BFRC demonstrates a 64.83% lower porosity (5.13%) than NC (11.66%) at 28 days, with microscopic analysis revealing a 77.5% proportion of harmless pores (<1.104 × 10⁷ μm³) in BFRC versus 67.6% in NC, driven by densified interfacial transition zones (ITZs). (3) Predictive Modeling: a two dimensional strength-porosity model and a three-dimensional age-dependent model are developed. The proposed multi-factor model demonstrates exceptional predictive capability (R² = 0.9994), establishing a quantitative relationship between pore micro structure and mechanical performance. The innovative pore extraction method and mathematical modeling approach offer valuable insights into the micro-structural evolution mechanism of fiber concrete. Full article

Plant roots influence various soil physical properties by altering the soil structure and pore configuration; however, a detailed understanding of these effects remains limited. In this study, we applied a relatively simple approach for segmenting plant roots and soil constituents using X-ray computed tomography (CT) images to evaluate root-induced changes in soil structure. The method combines manual initialization with a layer-wise automated region-growing approach, enabling the extraction of the root systems of soybean, Italian ryegrass, and Guinea grass. The method utilizes freely available software with a simple interface and does not require advanced image analysis skills, making it accessible to a wide range of researchers. The soil particles, pore water, and pore air were segmented using a Kriging-based thresholding technique. The segmented four-phase images allowed for the quantification of the volume fractions of soil constituents, pore size distributions, and coordination numbers. Furthermore, by separating the rhizosphere and bulk soil, we found that the root presence significantly reduced solid fractions and increased water content, particularly in the upper soil layers. Macropores and fine pores were observed near the roots, highlighting the complex structural impacts of root growth. While further validation is needed to assess the method’s applicability across different soil types and imaging conditions, it provides a practical basis for visualizing and quantifying root–soil interactions, and could contribute to advancing our understanding of how plant roots influence key soil hydraulic and thermal properties. Full article

The influence of porosity on rock mechanical properties constitutes a critical research focus. This investigation explores the relationship between pore structure parameters and mechanical characteristics through reconstructed numerical models. The study employs an integrated approach combining laboratory experiments and numerical simulations. Initially, high-resolution X-ray computed tomography (CT) was utilized to capture three-dimensional geometric features of Sichuan white sandstone microstructures, complemented by mechanical parameter acquisition through standardized testing protocols. The research workflow incorporated advanced image processing techniques, including adaptive total variation denoising algorithms for CT image enhancement and deep learning-based threshold segmentation for feature extraction. Subsequently, pore structure reconstruction models with controlled porosity variations were developed for systematic numerical experimentation. Key findings reveal a pronounced degradation trend in both mechanical strength and elastic modulus with increasing porosity levels. Based on simulation data, two empirical models were established: a porosity–compressive strength correlation model and a porosity–elastic modulus relationship model. These quantitative formulations provide theoretical support for understanding the porosity-dependent mechanical behavior in rock mechanics. The methodological framework and results presented in this study offer valuable insights for geological engineering applications and petrophysical characteristic analysis. Full article

The absence of standardized evaluation criteria for cold patch asphalt mixtures (CPAMs) leads to arbitrary material selection in pavement pothole repair, resulting in premature failure and recurrent damage. This study develops a comprehensive evaluation framework combining macro-performance tests with X-ray computed tomography (CT)-based meso-structural analysis. The macroscopic evaluation system incorporates six key parameters: aggregate gradation, binder–aggregate ratio, penetration strength, molding strength, residual rate, and stability retention. The CT-based meso-structural assessment quantifies void characteristics (longitudinal distribution, radial distribution, fractal dimension) and aggregate skeleton features (contact points, coordination number) through 3D reconstruction. Experimental results demonstrate that optimizing asphalt content (4.5–4.7%) with adjusted critical aggregate fractions (4.75 mm:35.0–45.0%; 2.36 mm:30.0–40.0%; 13.2 mm:1.0–1.2%; 9.5 mm:10.0–15.0%) significantly enhances repair durability. The established multiscale evaluation methodology provides a theoretical foundation for developing standardized CPAM quality specifications, particularly in emergency maintenance scenarios. Full article

This study aims to obtain findings on the internal water behavior, the presence of water channels, and the degree of washout due to rainfall infiltration in Japanese municipal solid waste (MSW) final disposal sites. Electrical resistivity tomography (ERT) monitoring and undistributed waste sampling for X-ray computed tomography (X-ray CT) analysis were conducted in the field. The study sites were targeted at Site A, which is mainly composed of non-combustible residues, and Site B, which is mainly composed of incineration ash. The time-dependent resistivity distributions obtained from real-time ERT monitoring were effective for us to understand the water content distribution after water infiltration during water injection tests. As a result, the global flow behavior and the local water channel flow were determined. In addition, X-ray CT analysis of the undisturbed waste samples obtained from the sites clarified the different pore structures, indicating the possibility of more advanced washing out at Site A than at Site B. Furthermore, the soil cover layer and gas extraction wells had a significant effect on the resistivity structure with respect to water flow behavior. Since soil cover layer and gas extraction wells are significant factors affecting waste stabilization by washout, it is suggested that these factors should be considered in the design and maintenance of landfills. Full article

Climate-induced wetting and drying (WD) cycles significantly affect the long-term performance of geotechnical structures. This study explores expansive Oxford clay’s mechanical and volumetric responses stabilised with ordinary Portland cement (OPC) and guar gum (GG) under repeated WD cycles. We prepared 108 samples in total—36 untreated, 36 treated with OPC, and 36 treated with GG. These samples were compacted to 90% of their maximum dry density and subjected to 1, 5, 10, and 15 WD cycles, with nine samples for each treatment at each cycle. During the WD cycles, we monitored volumetric strain and moisture content. Mechanical performance was assessed through unconsolidated undrained triaxial tests conducted at matric suctions of −1500 kPa, −33 kPa, and under saturated conditions. We evaluated the undrained shear strength (S_u), secant modulus of elasticity (E₅₀), and modulus of toughness (U_t). The results showed that OPC-treated samples consistently exhibited the highest S_u at −1500 kPa across all WD cycles, followed by untreated and GG-treated samples. At −33 kPa, OPC-treated samples again outperformed the others in S_u, while GG-treated samples performed better than the untreated ones. Under saturated conditions, GG-treated samples displayed a similar S_u to OPC-treated samples, significantly higher than untreated samples. Energy absorption capacity, measured through U_t, peaked for OPC-treated samples at −1500 kPa but favoured GG treatment at −33 kPa and under saturation. X-ray computed tomography (CT) revealed severe degradation in untreated samples, characterised by extensive cracking, minor cracking in OPC-treated samples, and minimal damage in GG-treated samples. This highlights the superior resilience of guar gum to wetting–drying cycles. These findings underscore the potential of guar gum as a sustainable alternative to cement for enhancing the WD resilience of expansive soils, particularly under low-suction or saturated conditions. Full article

Pores of different sizes and quantities are formed during the molding process of recycled aggregate concrete (RAC). However, few studies have examined the individual and combined effects of porosity and mesoscale pore size (pore size) on the axial compressive mechanical properties of RAC. In this study, the influence of porosity and pore size on the axial compressive mechanical behavior of RAC was examined by incorporating expanded polystyrene (EPS) particles to create prefabrication of pores. Additionally, crack development influenced by pores was analyzed using high-energy X-ray computed tomography (CT). Gray correlation analysis was employed to quantify the influence of pore size and porosity on compressive mechanical parameters. Furthermore, the combined effects of pore characteristics were assessed by introducing damage variables. It was shown that the compressive strength, strength reduction, elastic modulus, and modulus reduction exhibited linear correlations with porosity and exponential correlations with pore size. Cracks within the specimen predominantly propagate through the pores or along their edges. The influence of porosity on both strength and elastic modulus is more substantial than that of pore size. Moreover, the deterioration in mechanical properties is more pronounced when small pore size is coupled with high porosity, compared to the combination of large pore size and low porosity. Full article

Computed tomography scans are among the most used medical imaging modalities. With increased popularity and usage, the need for maintenance also increases. In this work, the problem is tackled using machine learning methods to create a predictive maintenance system for the classification of faulty X-ray tubes. Data for 137 different CT machines were collected, with 128 deemed to fulfil the quality criteria of the study. Of these, 66 have had X-ray tubes subsequently replaced. Afterwards, auto-regressive model coefficients and wavelet coefficients, as standard features in the area, are extracted. For classification, a set of different classical machine learning approaches is used alongside two different architectures of neural networks—1D VGG-style CNN and LSTM RNN. In total, seven different machine learning models are investigated. The best-performing model proved to be an LSTM trained on trimmed and normalised input data, with an accuracy of 87% and a recall of 100% for the faulty class. The developed model has the potential to maximise the uptime of CT machines and help mitigate the adverse effects of machine breakdowns. Full article