Search Results (321)

Wellbore stability is a crucial factor affecting the safe exploitation of offshore natural gas hydrates. As a sustainable energy source, natural gas hydrate has significant reserves, high energy density, and low environmental impact, making it an important candidate for alternative energy. Although research on the stability of screen pipes during horizontal-well hydrate production is currently limited, its importance in sustainable energy extraction is growing. This study therefore considers the effects of hydrate phase change, gas–water seepage, energy and mass exchange, reservoir deformation, and screen pipe influence and develops a coupled thermal–fluid–solid–chemical field model for horizontal-well natural gas hydrate production. The model results were validated using experimental data and standard test cases from the literature. The results obtained by applying this model in COMSOL Multiphysics 6.1 showed that the errors in all simulations were less than 2%, with errors of 12% and 6% observed at effective stresses of 0.5 MPa and 3 MPa, respectively. The simulation results indicate that the presence of the screen pipe in the hydrate reservoir exerts little effect on the decomposition of gas hydrates, but it effectively mitigates stress concentration in the near-wellbore region, redistributing the effective stress and significantly reducing the instability risk of the hydrate reservoir. Furthermore, the distribution of mechanical parameters around the screen pipe is uneven, with maximum values of equivalent Mises stress, volumetric strain, and displacement generally occurring on the inner side of the screen pipe in the horizontal crustal stress direction, making plastic instability most likely to occur in this area. With other basic parameters held constant, the maximum equivalent Mises stress and the instability area within the screen increase with the rise in the production pressure drop and wellbore size, and the decrease in screen pipe thickness. The results of this study lay the foundation for wellbore instability control in the production of offshore natural gas hydrates via depressurization. The study provides new insights into sustainable energy extraction, as improving wellbore stability during the extraction process can enhance resource utilization, reduce environmental impact, and promote sustainable development in energy exploitation. Full article

Optimizing printing parameters is crucial for enhancing the efficiency, surface quality, and dimensional accuracy of Fused Deposition Modeling (FDM) processes. A review of numerous publications reveals that most scholars analyze factors such as nozzle diameter and printing speed, while few investigate the impact of layer thickness, infill density, and shell layer count on print quality. Therefore, this study employed 3D slicing software to process the three-dimensional model and design printing process parameters. It systematically investigated the effects of layer thickness, infill density, and number of shells on printing time and geometric accuracy, quantifying the evaluation through volumetric error. Using an ABS connecting rod model, optimal parameters were determined within the defined range through orthogonal experimental design and signal-to-noise ratio (S/N) analysis. Subsequently, a backpropagation (BP) neural network was constructed to establish a predictive model for process optimization. Results indicate that parameter selection significantly impacts print duration and surface quality. Validation confirmed that the combination of 0.1 mm layer thickness, 40% infill density, and 5-layer shell configuration achieves the highest dimensional accuracy (minimum volumetric error and S/N value). Under this configuration, the volumetric error rate was 3.062%, with an S/N value of −9.719. Compared to other parameter combinations, this setup significantly reduced volumetric error, enhanced surface texture, and improved overall print precision. Statistical analysis indicates that the BP neural network model achieves a Mean Absolute Percentage Error (MAPE) of no more than 5.41% for volume error rate prediction and a MAPE of 5.58% for signal-to-noise ratio prediction. This validates the model’s high-precision predictive capability, with the established prediction model providing effective data support for FDM parameter optimization. Full article

Laminated bamboo (LB) represents a promising sustainable construction material, inheriting bamboo’s high strength, lightweight properties, and good ductility. However, the dimensional stability of mechanical performance—specifically size effects—remains a critical design challenge requiring systematic investigation. This study investigates the compression behavior of LB with tests of four specimen groups spanning volumes from 62,500 to 4,000,000 mm³ (25 × 25 × 100 mm to 100 × 100 × 400 mm). The research objectives encompass (i) characterizing compression behavior and failure mechanisms across different specimen scales, (ii) quantifying geometric and volumetric size effects on mechanical properties, (iii) evaluating theoretical frameworks for size effect prediction, (iv) developing progressive modeling approaches incorporating material heterogeneity, and (v) establishing design parameters for practical applications. Results demonstrate modest proportional size effects (1.60% strength reduction, 8.62% modulus reduction for 4× proportional scaling) but significant geometric optimization benefits, with cubic specimens achieving 15.78% higher strength and 25.11% greater modulus than equivalent-volume prismatic specimens. All specimens exhibited interfacial delamination failure with size-dependent crack propagation patterns. Theoretical analysis incorporates Weibull statistics, Bažant’s fracture mechanics, and Carpinteri’s fractal theory, with fracture energy modeling performing optimally. Three progressive modeling approaches achieve prediction accuracies ranging from 1.17% to 0.37% errors, with density-coupled modeling providing superior performance despite minimal density variations (COV = 9.27%). The research establishes size effect factors (0.86 for strength, 0.78 for modulus) and critical dimensions (125.64–126.14 mm), addressing critical gaps in LB size-dependent behavior. These parameters enable the development of reliable design methodologies for large-scale sustainable construction. Full article

We present a comparative analysis of three hybrid methodologies for transforming 3D kidney tumor segmentations of volumetric NIfTI data into highly accurate network representations. Exploiting the KiTS23 dataset, we evaluate edge-preserving reconstruction pipelines integrating anisotropic diffusion, multiscale Gaussian filtering and KNN-based network optimisation. Model 1 uses Gaussian smoothing with Marching Cubes, while Model 2 uses spline interpolation and Perona-Malik filtering for improved resolution. Model 3 extends this structure with normal sensitive vertex smoothing to preserve critical anatomical interfaces. Quantitative metrics (Dice score, HD₉₅) demonstrated the advantage of Model 3, which achieved a 22% reduction in the Hausdorff distance error rate compared to conventional methods while maintaining segmentation accuracy (Dice > 0.92). The proposed unsupervised pipeline bridges the gap between clinical interpretability and computational accuracy, providing a robust infrastructure for further applications in surgical planning and deep learning-based classification. Full article

This study introduces a novel approach to enhance the sustainability of road pavement construction by utilizing palm oil clinker (POC), an industrial waste product, as a replacement for fine aggregates (passing 4.75 mm) in stone mastic asphalt (SMA) mixtures. Departing from conventional practices, this research comprehensively evaluates the feasibility of using POC at varying replacement levels (0% to 100%) across a range of binder contents (5.0% to 7.0%). A significant contribution of this work is the application of Response Surface Methodology (RSM) to optimize the proportions of POC and binder content (BC), achieving target Marshall and volumetric properties for superior pavement performance. The results demonstrate that POC can effectively substitute fine aggregates in SMA mixtures, meeting all requirements for Marshall stability, flow, stiffness, and volumetric properties, even at a 100% replacement rate. Statistical analysis using RSM confirmed the model’s validity, exhibiting a high R-squared value (>0.80), significant p-values, and an adequate precision exceeding 4. Optimization analysis revealed that a 60% POC content with a 6% BC yields the most desirable combination for achieving optimal SMA mixture characteristics. Further validation through experimental testing showed a strong correlation with the theoretical RSM predictions, with an error margin below 5%. This research underscores the potential of POC as a sustainable alternative to traditional aggregates, paving the way for more environmentally friendly and cost-effective road construction practices while simultaneously addressing waste management challenges in the palm oil industry. Full article

Five-axis machining centers are essential for manufacturing complex, high-precision parts. However, their accuracy is significantly affected by thermally induced geometric errors, also known as thermo-elastic errors. This paper presents a comprehensive approach to thermal characterization and its potential application in predictive modeling on a five-axis machine tool demonstrator, showcasing the capabilities of a novel dynamic R-test measurement method. Based on a previously developed and validated dynamic R-test measurement method that enables the rapid, volumetric acquisition of machine deviations during continuous movement, detailed experimental investigations were conducted under various single- and combined-axis loading scenarios. The extensive dataset and detailed error information provided by the dynamic R-test method enabled thorough analysis and correlation of thermo-elastic errors, including translational and rotational errors, with temperature and control-internal axis data. A well-established phenomenological model based on PT1 transfer functions is used, detailing its input variables and parameter determination methods. The model’s predictive capability was rigorously validated against independent datasets, demonstrating significant reductions in primary errors (up to 70% in maximum residual and 80% in RMSE). This study identifies the most influential error types and their correlation with thermal loads. This confirms the feasibility of robustly predicting thermo-elastic behavior and enhancing the volumetric accuracy of five-axis machine tools, particularly by leveraging the detailed error insights enabled by the dynamic R-test. Full article

This work develops a Boundary Element Method (BEM) formulation for simulating bioheat transfer in perfused biological tissues using the Atangana–Baleanu fractional derivative in the Caputo sense (ABC). The ABC operator incorporates a nonsingular Mittag–Leffler kernel to model thermal memory effects while preserving compatibility with standard boundary conditions. The formulation combines boundary discretization with cell-based domain integration to account for volumetric heat sources, and a recursive time-stepping scheme to efficiently evaluate the fractional term. The model is applied to a one-dimensional cylindrical tissue domain subjected to metabolic heating and external energy deposition. Simulations are performed for multiple fractional orders, and the results are compared with classical BEM $(a=1.0)$, Caputo-based fractional BEM, and in vitro experimental temperature data. The fractional order $a\approx 0.894$ yields the best agreement with experimental measurements, reducing the maximum temperature error to 1.2% while maintaining moderate computational cost. These results indicate that the proposed BEM–ABC framework effectively captures nonlocal and time-delayed heat conduction effects in biological tissues and provides an efficient alternative to conventional fractional models for thermal analysis in biomedical applications. Full article

Background: Interstitial lung disease (ILD) is a frequent and potentially progressive manifestation in patients with connective tissue diseases (CTDs). Accurate and reproducible quantification of parenchymal abnormalities on high-resolution computed tomography (HRCT) is essential for evaluating treatment response and monitoring disease progression, particularly in complex cases undergoing antifibrotic therapy. Artificial intelligence (AI)-based tools may improve consistency in visual assessment and assist less experienced radiologists in longitudinal follow-up. Methods: In this retrospective study, 48 patients with CTD-related ILD receiving antifibrotic treatment were included. Each patient underwent four HRCT scans, which were evaluated independently by two radiologists (one expert, one non-expert) using a semi-quantitative scoring system. Percentage estimates of lung involvement were assigned for four parenchymal patterns: hyperlucency, ground-glass opacity (GGO), reticulation, and honeycombing. AI-based analysis was performed using the Imbio Lung Texture Analysis platform, which generated continuous volumetric percentages for each pattern. Concordance between AI and human interpretation was assessed, along with mean absolute error (MAE) and inter-reader differences. Results: The AI-based system demonstrated high concordance with the expert radiologist, with an overall agreement of 81% across patterns. The MAE between AI and the expert ranged from 1.8% to 2.6%. In contrast, concordance between AI and the non-expert radiologist was significantly lower (60–70%), with higher MAE values (3.9% to 5.2%). McNemar’s and Wilcoxon tests confirmed that AI aligned more closely with the expert than the non-expert reader (p < 0.01). AI proved particularly effective in detecting subtle changes in parenchymal burden during follow-up, especially when visual interpretation was inconsistent. Conclusions: AI-driven quantitative imaging offers performance comparable to expert radiologists in assessing ILD patterns on HRCT and significantly outperforms less experienced readers. Its reproducibility and sensitivity to change support its role in standardizing follow-up evaluations and enhancing multidisciplinary decision-making in patients with CTD-related ILD, particularly in progressive fibrosing cases receiving antifibrotic therapy. Full article

Herein, we report a USB-powered VR-HMD prototype integrated with our 33 mm aperture varifocal liquid lenses and electronic drive components, all assembled in a conventional VR-HMD form-factor. In this volumetric-display-based VR system, a sequence of virtual images are rapidly flash-projected at different plane depths in front of the observer and are synchronized with the correct accommodations provided by the varifocal lenses for depth-matched focusing at chosen sweep frequency. This projection mechanism aids in resolving the VAC that is present in conventional fixed-depth VR. Additionally, this system can address refractive error corrections like myopia and hyperopia for prescription users and do not require any eye-tracking systems. We experimentally demonstrate these lenses can vibrate up to frequencies approaching 100 Hz and report the frequency response of the varifocal lenses and their focal characteristics in real time as a function of the drive frequency. When integrated with the prototype’s 120 fps VR display system, these lenses produce a net diopter change of 2.3 D at a sweep frequency of 45 Hz while operating at ~70% of its maximum actuation voltage. The components add a total weight of around 50 g to the off-the-shelf VR set, making it a cost-effective but lightweight minimal solution. Full article

This study developed an inertial measurement unit (IMU)-enhanced bidirectional reflectance distribution function (BRDF) imaging system to address angular errors in multi-angle polarimetric measurements. The system integrates IMU-based closed-loop feedback, motorized motion, and image calibration, achieving zenith angle error reduction of up to 1.2° and angular control precision of approximately 0.05°. With a modular and lightweight structure, it supports rapid deployment in field scenarios, while the 2000 mm rail span enables detection of large-scale targets and three-dimensional reconstruction beyond the capability of conventional tabletop devices. Experimental evaluations on six representative materials show that compared with mark-based reference angles, IMU feedback consistently improves polarimetric accuracy. Specifically, the degree of linear polarization (DoLP) mean deviations are reduced by about 5–12%, while standard deviation fluctuations are suppressed by 20–40%, enhancing measurement repeatability. For the angle of polarization (AoP), IMU feedback decreases mean errors by 10–45% and lowers standard deviations by 10–37%, ensuring greater spatial phase continuity even under high-reflection conditions. These results confirm that the proposed system not only eliminates systematic angular errors but also achieves robust stability in global measurements, providing a reliable technical foundation for material characterization, machine vision, and volumetric reconstruction. Full article

High-temperature heat pumps (HTHPs) have gradually begun to play an essential role in using heat in industry for waste heat recovery and providing higher-grade heat. The isentropic efficiency and volumetric efficiency of HTHPs are significantly affected by high-temperature operating conditions, which take the pressure ratio (PR) as the key parameter, with limited consideration of other factors such as temperature. Relying on the experimental data obtained from the industrial-grade HTHP system experimental platform, this work proposed an XGBoost-MLR algorithm-based method to identify the key parameters of HTHP isentropic efficiency and volumetric efficiency. High-precision (R² > 0.95) prediction models were established to determine the effect of temperature variables on isentropic efficiency and volumetric efficiency. After the key parameters were identified, the empirical equation of isentropic efficiency and volumetric efficiency applicable to this operation condition were constructed. The average relative errors of the two empirical formulas were 5.95% and 5.28%, respectively. Finally, the generalizability of empirical formulas was verified using experimental data from other researchers. The isentropic empirical formula had a relative deviation of less than 10% under twin-screw compressor conditions. However, the applicability of the volumetric efficiency empirical formula was unstable in compressors of different sizes. The feasibility of the method was also discussed. Full article

Because artificially cemented granular (ACG) materials employ diverse combinations of aggregates and binders—including cemented soil, low-cement-content cemented sand and gravel (LCSG), and concrete—their stress–strain responses vary widely. In LCSG, the binder dosage is typically limited to 40–80 kg/m³ and the sand–gravel skeleton is often obtained directly from on-site or nearby excavation spoil, endowing the material with a markedly lower embodied carbon footprint and strong alignment with current low-carbon, green-construction objectives. Yet, such heterogeneity makes a single material-specific constitutive model inadequate for predicting the mechanical behavior of other ACG variants, thereby constraining broader applications in dam construction and foundation reinforcement. This study systematically summarizes and analyzes the stress–strain and volumetric strain–axial strain characteristics of ACG materials under conventional triaxial conditions. Generalized hyperbolic and parabolic equations are employed to describe these two families of curves, and closed-form expressions are proposed for key mechanical indices—peak strength, elastic modulus, and shear dilation behavior. Building on generalized plasticity theory, we derive the plastic flow direction vector, loading direction vector, and plastic modulus, and develop a concise, transferable elastoplastic model suitable for the full spectrum of ACG materials. Validation against triaxial data for rock-fill materials, LCSG, and cemented coal–gangue backfill shows that the model reproduces the stress and deformation paths of each material class with high accuracy. Quantitative evaluation of the peak values indicates that the proposed constitutive model predicts peak deviatoric stress with an error of 1.36% and peak volumetric strain with an error of 3.78%. The corresponding coefficients of determination R² between the predicted and measured values are 0.997 for peak stress and 0.987 for peak volumetric strain, demonstrating the excellent engineering accuracy of the proposed model. The results provide a unified theoretical basis for deploying ACG—particularly its low-cement, locally sourced variants—in low-carbon dam construction, foundation rehabilitation, and other sustainable civil engineering projects. Full article

Volumetric error decoupling is a critical prerequisite for effective error compensation. In this paper, the forward volumetric error model is established using the screw theory. Additionally, the Jacobian matrix based on the product of exponential is derived to construct the linear relationship between the volumetric error and the axis motion and decouple the volumetric error model. To address the limitation of compensation motion, a step-by-step decoupling method is proposed, where attitude and position errors are compensated sequentially. After detecting the actual geometric errors of the grinding machine, the volumetric error can be determined, and the compensation motion commands for each axis are calculated to correct the volumetric error. The simulation result shows that the mean value of the comprehensive error ranges can be reduced from 19.7 μm to 1.8 μm, demonstrating the effectiveness of the proposed method. Full article

Soil organic carbon (SOC) monitoring is central to carbon farming Monitoring, Reporting, and Verification (MRV), yet high laboratory costs and sparse sampling limit its scalability. We present the first independent field validation of the Stenon FarmLab multi-sensor probe across 100 temperate European arable-soil samples, benchmarking its default outputs and a simple pH-corrected model against three laboratory reference methods: acid-treated TOC, temperature-differentiated TOC (SoliTOC), and total carbon dry combustion. Uncorrected FarmLab algorithms systematically overestimated SOC by +0.20% to +0.27% (SD = 0.25–0.28%), while pH adjustment reduced bias to +0.11% and tightened precision to SD = 0.23%. Volumetric moisture had no significant effect on measurement error (r = −0.14, p = 0.16). Bland–Altman and Deming regression demonstrated improved agreement after pH correction, but formal equivalence testing (accuracy, precision, concordance) showed that no in-field model fully matched laboratory standards—the pH-corrected variant passed accuracy and concordance evaluation yet failed the precision criterion (p = 0.0087). At ~EUR 3–4 per measurement versus ~EUR 44 for lab analysis, FarmLab facilitates dense spatial sampling. We recommend a hybrid monitoring strategy combining routine, pH-corrected in-field mapping with laboratory-based recalibrations alongside expanded calibration libraries, integrated bulk density measurement, and adaptive machine learning to achieve both high-resolution and certification-grade rigor. Full article

Metaconcrete (MC) is a class of engineered cementitious composites that integrates locally resonant inclusions to filter stress waves. While the dynamic benefits are well established, the effect of resonator content and geometry on static compressive resistance remains unclear. This study develops the first two-dimensional mesoscale finite-element model that explicitly represents steel cores, rubber coatings, and interfacial transition zones to predict the quasi-static behavior of MC. The model is validated against benchmark experiments, reproducing the 56% loss of compressive strength recorded for a 10.6% resonator volume fraction with an error of less than 1%. A parametric analysis covering resonator ratios from 1.5% to 31.8%, diameters from 16.8 mm to 37.4 mm, and coating thicknesses from 1.0 mm to 4.2 mm shows that (i) strength decays exponentially with volumetric content, approaching an asymptote at ≈20% of plain concrete strength; (ii) larger cores with thinner coatings minimize stiffness loss (<10%) while limiting strength reduction to 15–20%; and (iii) material properties of the resonator have a secondary influence (<6%). Two closed-form expressions for estimating MC strength and Young’s modulus (R² = 0.83 and 0.94, respectively) are proposed to assist with the preliminary design. The model and correlations lay the groundwork for optimizing MC that balances vibration mitigation with structural capacity. Full article