Search Results (12,009)

Objectives: Spinal diseases are commonly encountered health problems with a wide spectrum. In addition to degenerative changes, other common spinal pathologies include metastases and compression fractures. Benign tumors like hemangiomas and infections such as spondylodiscitis are also frequently observed. Although magnetic resonance imaging (MRI) is considered the gold standard in diagnostic imaging, the morphological similarities of lesions can pose significant challenges in differential diagnoses. In recent years, the use of artificial intelligence applications in medical imaging has become increasingly widespread. In this study, we aim to detect and classify vertebral body lesions using the YOLO-v8 (You Only Look Once, version 8) deep learning architecture. Materials and Methods: This study included MRI data from 235 patients with vertebral body lesions. The dataset comprised sagittal T1- and T2-weighted sequences. The diagnostic categories consisted of acute compression fractures, metastases, hemangiomas, atypical hemangiomas, and spondylodiscitis. For automated detection and classification of vertebral lesions, the YOLOv8 deep learning model was employed. Following image standardization and data augmentation, a total of 4179 images were generated. The dataset was randomly split into training (80%) and validation (20%) subsets. Additionally, an independent test set was constructed using MRI images from 54 patients who were not included in the training or validation phases to evaluate the model’s performance. Results: In the test, the YOLOv8 model achieved classification accuracies of 0.84 and 0.85 for T1- and T2-weighted MRI sequences, respectively. Among the diagnostic categories, spondylodiscitis had the highest accuracy in the T1 dataset (0.94), while acute compression fractures were most accurately detected in the T2 dataset (0.93). Hemangiomas exhibited the lowest classification accuracy in both modalities (0.73). The F1 scores were calculated as 0.83 for T1-weighted and 0.82 for T2-weighted sequences at optimal confidence thresholds. The model’s mean average precision (mAP) 0.5 values were 0.82 for T1 and 0.86 for T2 datasets, indicating high precision in lesion detection. Conclusions: The YOLO-v8 deep learning model we used demonstrates effective performance in distinguishing vertebral body metastases from different groups of benign pathologies. Full article

Background: A critical review of the literature demonstrates that masticatory apparatus with an artificial oral environment is of interest in the fields including (i) dental science; (ii) food science; (iii) the pharmaceutical industries for drug release. However, apparatus that closely mimics human chewing and oral conditions has yet to be realised. This study investigates the vital role of dental morphology and form–function connections using two-bite test parameters for effective drug release from medicated chewing gum (MCG) and compares them to human chewing efficiency with the aid of a humanoid chewing robot and a bionics product lifecycle management (PLM) framework with built-in reverse biomimetics—both developed by the first author. Methods: A novel, bio-engineered two-bite testbed is created for two testing machines with compression and torsion capabilities to conduct two-bite tests for evaluating the mechanical properties of MCGs. Results: Experimental studies are conducted to investigate the relationship between biting force and crushing/shearing and understand chewing efficiency and effective mastication. This is with respect to mechanochemistry and power stroke for disrupting mechanical bonds releasing the active pharmaceutical ingredients (APIs) of MCGs. The manuscript discusses the effect and the critical role that jaw physiology, dental morphology, the Bennett angle of mandible (BA) and the Frankfort-mandibular plane angle (FMA) on two-bite test parameters when FMA = 0, 25 or 29.1 and BA = 0 or 8. Conclusions: The impact on other scientific fields is also explored. Full article

In this study, to solidify the silt in an expressway, a stabilizing agent composed of industrial wastes, such as ordinary Portland cement (OPC), calcium based alkaline activator (CAA), silicate solid waste material (SISWM) and sulfate solid waste material (SUSWM) was developed. Orthogonal experiments and comparative experiments were carried out to analyze the strength and water stability of the stabilized silt, and get the optimal proportion of each component in the stabilizing agent. A series of laboratory tests, including unconfined compressive strength (UCS), water stability (WS), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and X-ray diffraction (XRD) analyses, were conducted on solidified silt samples treated with the stabilizing agent at optimal mixing ratios of OPC, CAA, SISWM, and SUSWM to elucidate the evolution of mineral composition and microstructure. Full article

CO₂ transport is a crucial part of CCUS. Nonetheless, due to the physical property differences between CO₂ and natural gas and oil, CO₂ pipeline transport is distinct from natural gas and oil transport. Gaseous CO₂ transportation has become the preferred scheme for transporting impurity-containing CO₂ tail gas in purification plants due to its advantages of simple technology, low cost, and high safety, which are well suited to the scenarios of low transportation volume and short distance in purification plants. The research on its physical property and state parameters is precisely aimed at optimizing the process design of gaseous transportation so as to further improve transportation efficiency and safety. Therefore, it has important engineering practical significance. Firstly, this paper collected and analyzed the research cases of CO₂ transport both domestically and internationally, revealing that phase state and physical property testing of CO₂ gas containing impurities is the basic condition for studying CO₂ transport. Subsequently, the exhaust gas captured by the purification plant was captured after hydrodesulfurization treatment, and the characteristics of the exhaust gas components were obtained by comparing before and after treatment. By preparing fluid samples with varied CO₂ content and conducting the flash evaporation test and PV relationship test, the compression factor and density of natural gas under different temperatures and pressures were obtained. It is concluded that under the same pressure in general, the higher the CO₂ content, the smaller the compression factor. Except for pure CO_2, the higher the CO₂ content, the higher the density under constant pressure, which is related to the content of C₂ and heavier hydrocarbon components. At the same temperature, the higher the CO₂ content, the higher the viscosity under the same pressure; the lower the pressure, the slower the viscosity growth slows down. The higher the CO₂ content at the same temperature, the higher the specific heat at constant pressure. With the decrease in temperature, the CO₂ content reaching the highest specific heat at the identical pressure gradually decreases. Finally, BWRS, PR, and SRK equations of state were utilized to calculate the compression factor and density of the gas mixture with a molar composition of 50% CO₂ and the gas mixture with a molar composition of 100% CO₂. Compared with the experimental results, the most suitable equation of state is selected as the PR equation, which refers to the parameter setting of critical nodes of CO₂ gas transport. Full article

In this study, edge-oxidized graphene oxide (EOGO) was used as an additive in fly ash (FA) geopolymer paste. The effect of EOGO on the properties of the fly ash geopolymer was investigated. EOGO was added to the FA geopolymer at four different percentages (0%, 0.1%, 0.5% and 1%), and the mixture was cured under two different conditions: room curing (~20 °C) and heat curing (~60 °C). To characterize the FA-EOGO geopolymer, multiple laboratory tests were employed, including compressive strength, Free-Free Resonance Column (FFRC), density, water absorption, and setting tests. The FFRC test was used to evaluate the stiffness at small strain (Young’s modulus) via the resonance of the specimen. The mechanical test results showed that the strength and elastic modulus were high during heat curing, and the highest compressive strength and elastic modulus were achieved at 0.1% EOGO. In the physical test, 0.1% EOGO had the highest density and the lowest porosity and water absorption. As a result of the setting time test, as the EOGO content increased, the setting time was shortened. It is concluded that the optimum proportion of EOGO is 0.1% in FA geopolymer paste. Full article

This present study proposes different machine learning-based predictors for the assessment of the residual compressive strength of Self-Compacting Concrete (SCC) subjected to high temperatures. The investigation is based on several literature algorithmic approaches based on Artificial Neural Networks with distinct training algorithms (Bayesian Regularization, Levenberg–Marquardt, Scaled Conjugate Gradient, and Resilient Backpropagation), Support Vector Regression, and Random Forest methods. A training database of 150 experimental data points is derived from a careful literature review, incorporating temperature (20–800 °C), geometric ratio (height/diameter), and corresponding compressive strength values. A statistical analysis revealed complex non-linear relationships between variables, with strong negative correlation between temperature and strength and heteroscedastic data distribution, justifying the selection of advanced machine learning techniques. Feature engineering improved model performance through the incorporation of quadratic terms, interaction variables, and cyclic transformations. The Resilient Backpropagation algorithm demonstrated superior performance with the lowest prediction errors, followed by Bayesian Regularization. Support Vector Regression achieved competitive accuracy despite its simpler architecture. Experimental validation using specimens tested up to 800 °C showed a good reliability of the developed systems, with prediction errors ranging from 0.33% to 23.35% across different temperature ranges. Full article

Compressed sensing is widely used in modern resource-constrained sensor networks. However, achieving high-quality and robust signal reconstruction under low sampling rates and noise interference remains challenging. Traditional CS methods have limited performance, so many deep learning-based CS models have been proposed. Although these models show strong fitting capabilities, they often lack the ability to handle complex noise in sensor networks, which affects their performance stability. To address these challenges, this paper proposes SBCS-Net. This framework innovatively expands the iterative process of sparse Bayesian compressed sensing using convolutional neural networks and Transformer. The core of SBCS-Net is to optimize key SBL parameters through end-to-end learning. This can adaptively improve signal sparsity and probabilistically process measurement noise, while fully leveraging the powerful feature extraction and global context modeling capabilities of deep learning modules. To comprehensively evaluate its performance, we conduct systematic experiments on multiple public benchmark datasets. These studies include comparisons with various advanced and traditional compressed sensing methods, comprehensive noise robustness tests, ablation studies of key components, computational complexity analysis, and rigorous statistical significance tests. Extensive experimental results consistently show that SBCS-Net outperforms many mainstream methods in both reconstruction accuracy and visual quality. In particular, it exhibits excellent robustness under challenging conditions such as extremely low sampling rates and strong noise. Therefore, SBCS-Net provides an effective solution for high-fidelity, robust signal recovery in sensor networks and related fields. Full article

To address the limitations of traditional hardened concrete road surfaces in coal mine tunnels, which are prone to damage and entail high maintenance costs, this study proposes using modular concrete blocks composed of fly ash and coal gangue as an alternative to conventional materials. These blocks offer advantages including ease of construction and rapid, straightforward maintenance, while also facilitating the reuse of substantial quantities of solid waste, thereby mitigating resource wastage and environmental pollution. Initially, the mineral composition of the raw materials was analyzed, confirming that although the physical and chemical properties of Liangshui Well coal gangue are slightly inferior to those of natural crushed stone, they still meet the criteria for use as concrete aggregate. For concrete blocks incorporating 20% fly ash, the steam curing process was optimized with a recommended static curing period of 16–24 h, a temperature ramp-up rate of 20 °C/h, and a constant temperature of 50 °C maintained for 24 h to ensure optimal performance. Orthogonal experimental analysis revealed that fly ash content exerted the greatest influence on the compressive strength of concrete, followed by the additional water content, whereas the aggregate particle size had a comparatively minor effect. The optimal mix proportion was identified as 20% fly ash content, a maximum aggregate size of 20 mm, and an additional water content of 70%. Performance testing indicated that the fabricated blocks exhibited a compressive strength of 32.1 MPa and a tensile strength of 2.93 MPa, with strong resistance to hydrolysis and sulfate attack, rendering them suitable for deployment in weakly alkaline underground environments. Considering the site-specific conditions of the Liangshuijing coal mine, ANSYS 2020 was employed to simulate and analyze the mechanical behavior of the blocks under varying loads, thicknesses, and dynamic conditions. The findings suggest that hexagonal coal gangue blocks with a side length of 20 cm and a thickness of 16 cm meet the structural requirements of most underground mine tunnels, offering a reference model for cost-effective paving and efficient roadway maintenance in coal mines. Full article

Accurate calibration of mesoscopic contact model parameters is essential for ensuring the reliability of Particle Flow Code in Three Dimensions (PFC3D) simulations in geotechnical engineering. Trial-and-error approaches are often used to determine the parameters of the contact model, but they are time-consuming, labor-intensive, and offer no guarantee of parameter validity or simulation credibility. Although conventional machine learning techniques have been applied to invert the contact model parameters, they are hampered by the difficulty of selecting the optimal hyperparameters and, in some cases, insufficient data, which limits both the predictive accuracy and robustness. In this study, a total of 361 PFC3D uniaxial compression simulations using a linear parallel bond model with varied mesoscopic parameters were generated to capture a wide range of rock and geotechnical material behaviors. From each stress–strain curve, eight characteristic points were extracted as inputs to a multi-objective Automated Machine Learning (AutoML) model designed to invert three key mesoscopic parameters, i.e., the elastic modulus (E), stiffness ratio (k_s/k_n), and degraded elastic modulus (E_d). The developed AutoML model, comprising two hidden layers of 256 and 32 neurons with ReLU activation function, achieved coefficients of determination (R²) of 0.992, 0.710, and 0.521 for E, k_s/k_n, and E_d, respectively, demonstrating acceptable predictive accuracy and generalizability. The multi-objective AutoML model was also applied to invert the parameters from three independent uniaxial compression tests on rock-like materials to validate its practical performance. The close match between the experimental and numerically simulated stress–strain curves confirmed the model’s reliability for mesoscopic parameter inversion in PFC3D. Full article

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

Fabric veneer panels were prepared using ethylene-vinyl acetate copolymer film (EVA) as the intermediate layer and poplar plywood as the substrate. Eight fabrics with different compositions were selected for evaluation to screen out fabric materials suitable for poplar plywood veneer. The fabrics were objectively analyzed by bending and draping, compression, and surface roughness, and subjectively evaluated by establishing seven levels of semantic differences. ESEM, surface adhesive properties, and peel resistance tests were used to characterize the microstructure and physical–mechanical properties of the composites. The results show that cotton and linen fabrics and corduroy fabrics are superior to other fabrics in performance, and they are suitable for decorative materials. Because the fibers of the doupioni silk fabric are too thin, and the fibers of felt fabric are randomly staggered, they are not suitable for the surface decoration materials of man-made panels. The acetate veneer surface gluing performance was 1.31 MPa, and the longitudinal peel resistance was 20.98 N, significantly exceeding that of other fabric veneers. Through the subjective and objective analysis of fabrics and gluing performance tests, it was concluded that, compared with fabrics made of natural fibers, man-made fiber fabrics are more suitable for use as surface finishing materials for wood-based panels. The results of this study provide a theoretical basis and process reference for the development of environmentally friendly decorative panels, which can be expanded and applied to furniture, interior decoration, and other fields. Full article

Refractory high-entropy alloys (HEAs) are promising candidates for next-generation nuclear applications, particularly fusion reactors, due to their excellent high-temperature mechanical properties and irradiation resistance. Here, the microstructure and mechanical behavior were investigated for an equimolar WTaTiVZr HEA, designed from a palette of low-activation elements. The as-cast alloy exhibited a dendritic microstructure composed of W-Ta rich dendrites and Zr-Ti-V rich inter-dendritic regions, both possessing a body-centered cubic (BCC) crystal structure. Room temperature bulk compression tests showed ultra-high strength of around 1.6 GPa and plastic strain ~6%, with fracture surfaces showing cleavage facets. The alloy also demonstrated excellent high-temperature strength of ~650 MPa at 500 °C. Scratch-based fracture toughness was ~38 MPa√m for the as-cast WTaTiVZr HEA compared to ~25 MPa√m for commercially used pure tungsten. This higher value of fracture toughness indicates superior damage tolerance relative to commercially used pure tungsten. These results highlight the alloy’s potential as a low-activation structural material for high-temperature plasma-facing components (PFCs) in fusion reactors. Full article

In this study, the thermal effectiveness of thermally conductive concrete pavements (TCPs) using silicon carbide (SiC) as a fine aggregate replacement was investigated, compared with that of ordinary Portland cement pavements (OPCPs). The most important purpose of this study is to improve the thermal performance of concrete pavement. Additionally, this study utilized improved thermal properties to enhance the efficiency of pavement heating to prevent icing and snow stacking. Both mixtures met the Korean standards for air content (4.5–6%) and slump (80–150 mm), demonstrating adequate workability. TCP exhibited a higher mechanical performance, with average compressive and flexural strengths of 42.88 MPa and 7.35 MPa, respectively, exceeding the required targets of a 30 MPa compressive strength and a 4.5 MPa flexural strength. The improved strength was mainly attributed to the filler effect and partly due to the van der Waals interactions of the SiC particles. Thermal conductivity tests showed a significant improvement in the TCP (3.20 W/mK), which was approximately twice that of OPCP (1.59 W/mK), indicating an enhanced heat transfer efficiency. In winter field tests, TCP effectively maintained high surface temperatures, overcoming heat loss and outperforming the OPCP. In the site experiment, thermal efficiency was clearly shown in the temperature at the center of the TCP, which was 3.5 °C higher than at the center of the OPCP at the coldest time. These improvements suggest that SiC-reinforced concrete pavements can be practically utilized for effective snow removal and ice mitigation in road systems. Full article

Atmospheric climate science lacks the capacity to integrate thermodynamics with the gravitational potential of air in a classical quantum theory. To what extent can we identify Carnot’s ideal heat engine cycle in reversible isothermal and isentropic phases between dual temperatures partitioning heat flow with coupled work processes in the atmosphere? Using statistical action mechanics to describe Carnot’s cycle, the maximum rate of work possible can be integrated for the working gases as equal to variations in the absolute Gibbs energy, estimated as sustaining field quanta consistent with Carnot’s definition of heat as caloric. His treatise of 1824 even gave equations expressing work potential as a function of differences in temperature and the logarithm of the change in density and volume. Second, Carnot’s mechanical principle of cooling caused by gas dilation or warming by compression can be applied to tropospheric heat–work cycles in anticyclones and cyclones. Third, the virial theorem of Lagrange and Clausius based on least action predicts a more accurate temperature gradient with altitude near 6.5–6.9 °C per km, requiring that the Gibbs rotational quantum energies of gas molecules exchange reversibly with gravitational potential. This predicts a diminished role for the radiative transfer of energy from the atmosphere to the surface, in contrast to the Trenberth global radiative budget of ≈330 watts per square metre as downwelling radiation. The spectral absorptivity of greenhouse gas for surface radiation into the troposphere enables thermal recycling, sustaining air masses in Lagrangian action. This obviates the current paradigm of cooling with altitude by adiabatic expansion. The virial-action theorem must also control non-reversible heat–work Carnot cycles, with turbulent friction raising the surface temperature. Dissipative surface warming raises the surface pressure by heating, sustaining the weight of the atmosphere to varying altitudes according to latitude and seasonal angles of insolation. New predictions for experimental testing are now emerging from this virial-action hypothesis for climate, linking vortical energy potential with convective and turbulent exchanges of work and heat, proposed as the efficient cause setting the thermal temperature of surface materials. Full article

The durability and fire resilience of concrete structures are increasingly critical in modern construction, particularly under elevated-temperature exposure. With this context, the current study explores the thermal and microstructural characteristics of geopolymer-based ultra-high-performance concrete (G-UHPC) incorporating quartz powder (QP) and silica fume (SF) after exposure to elevated temperatures. SF was used at 15% and 30% to partially replace the precursor material, while QP was used at 25%, 30%, and 35% as a partial replacement for fine sand. The prepared specimens were exposed to 200 °C, 400 °C, and 800 °C, followed by air cooling. Mechanical strength tests were conducted to evaluate compressive and flexural strengths, as well as failure patterns. Microstructural changes due to thermal exposure were assessed using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS). Among the prepared mixtures, the 30SF35QP mixture exhibited the highest compressive strength (156.0 MPa), followed by the 15SF35QP mix (146.83 MPa). The experimental results demonstrated that G-UHPC underwent varying levels of thermal degradation across the 200–800 °C range yet displayed excellent resistance to thermal spalling. At 200 °C, compressive strength increased due to enhanced geopolymerization, with the control mix showing a 29.8% increase. However, significant strength reductions were observed at 800 °C, where the control mix retained only 30.8% (32.0 MPa) and the 30SF25QP mixture retained 28% (38.0 MPa) of their original strengths. Despite increased porosity and cracking at 800 °C, the 30SF35QP mixture exhibited superior strength retention due to its denser matrix and reduced voids. The EDS results confirmed improved gel stability in the 30% SF mixtures, as evidenced by higher silicon content. These findings suggest that optimizing SF and QP content significantly enhances the fire resistance and structural integrity of G-UHPC, providing practical insights for the design of sustainable, high-performance concrete structures in fire-prone environments. Full article