Search Results (1,432)

This study presents the design and performance assessment of suction anchor foundations for an integrated offshore wind–solar–aquaculture system located in Jiangsu Sheyang, China. The project represents one of the first practical demonstrations of coupling renewable energy production with large-scale marine aquaculture on a shared floating platform. Using three-dimensional numerical simulations in FLAC3D and ABAQUS, the study evaluates the anchors’ bearing capacity, structural safety, and fatigue performance under ultimate (ULS), accidental (ALS), and fatigue (FLS) limit states. The analysis incorporates site-specific geotechnical conditions, seabed scour, and installation deviations, providing a realistic framework for foundation design in layered coastal sediments. Results confirm that the suction anchor system meets international safety requirements (DNV, CCS) and maintains robust performance throughout its service life. The findings demonstrate that scour depth and installation accuracy are critical factors governing anchor reliability and offer practical insights for updating offshore foundation design standards in future multifunctional renewable–aquaculture developments. Full article

This paper presents a lower-limb hip exoskeleton system integrated with an adjustable-stiffness flexible energy-storage module for three-dimensional gait correction. This system features a modular flexible mechanical design and a stiffness-gain scheduled PID control strategy for dynamic, personalized assistance. Based on biomechanical analysis of the hip joint, a 3D gait correction model was constructed targeting impairments in flexion, abduction, and adduction. The control strategy adjusts system stiffness in real-time according to gait phase and user-specific parameters. Experimental results demonstrated that the exoskeleton effectively reduced joint trajectory variability (22% decrease in standard deviation of hip flexion angle) and improved muscle activation patterns (21.4% increase in rectus femoris activity), thereby enhancing gait symmetry and stability. This study offers a feasible mechatronic solution for pathological gait correction with promising clinical applicability. Full article

The demand for organ transplantation continues to rise worldwide, intensifying the gap between supply and demand and driving research in tissue engineering (TE). Bioprinting, particularly light-based vat photopolymerization (VP) methods such as digital light processing (DLP), has emerged as a promising strategy to fabricate complex, cell-compatible tissue constructs with high precision. In this study, we developed an open-source, bottom-up DLP bioprinter designed to provide a cost-effective and modular alternative to commercial systems. The device was built from commercially available components and custom-fabricated parts, with tolerance allocation and deviation analyses applied to ensure structural reliability. Mechanical and optical subsystems were modeled and validated, and the control architecture was implemented on the Arduino platform with a custom Python-based graphical interface. The system achieved a theoretical Z-axis resolution of 1 μm and a vertical travel range of 50 mm, with accuracy and repeatability comparable to research-grade bioprinters. Initial printing trials using polyethylene glycol diacrylate (PEGDA) hydrogels demonstrated high-fidelity microfluidic constructs with adequate dimensional precision. Collectively, these results validate the functionality of the proposed system and highlight its potential as a flexible, precise, and cost-effective platform that is also easy to customize to advance the democratization of biofabrication in TE. Full article

Background/Objective: Precise preprocedural planning is essential for the safety and efficacy of structural heart interventions. Conventional imaging modalities, while informative, do not allow for direct and accurate visualization, limiting procedural predictability. We aimed to develop and validate a high-resolution micro-computed tomography (microCT)-based reverse modeling workflow that integrates digital reconstructions of metallic cardiac devices into patient imaging datasets, enabling accurate, patient-specific virtual simulation for procedural planning. Methods: Clinical-grade transcatheter heart valves, septal defect occluders, patent ductus arteriosus occluders, left atrial appendage closure devices, and coronary stents were scanned using microCT (36.9 μm resolution). Agreement was assessed by intra-class correlation coefficients (ICC) and Bland–Altman analyses. Device geometries were reconstructed into 3D stereolithography files and virtually implanted within multislice CT datasets using dedicated software. Results: Devices were successfully reverse-modeled with high geometric fidelity, showing negligible dimensional deviations from manufacturer specifications (mean ΔDistance range: −0.20 to +0.20 mm). Simulated measurements demonstrated excellent concordance with postprocedural imaging (ICC 0.90–0.96). The workflow accurately predicted clinically relevant parameters such as valve-to-coronary distances and implantation depths. Notably, preprocedural simulation identified a case at high risk of coronary obstruction, confirmed clinically and managed successfully. Conclusions: The microCT-based reverse modeling workflow offers a rapid, reproducible, and clinically relevant method for patient-specific simulation in structural heart interventions. By preserving anatomical fidelity and providing detailed device–tissue spatial visualization, this approach enhances preprocedural planning accuracy, risk stratification, and procedural safety. Its resource-efficient digital nature facilitates broad adoption and iterative simulation. Full article

The efficient and non-destructive inspection of the chlorophyll content of grey jujube leaf is of great significance for its growth surveillance and nutritional diagnosis. Near-infrared spectroscopy combined with chemometric methods provides an effective approach to achieve this goal. This study took grey jujube leaves as the research object, systematically collected near-infrared spectral data in the range of 4000–10,000 cm⁻¹, and simultaneously measured their soil and plant analyzer development (SPAD) value as a reference index for chlorophyll content. Through various pretreatment and their combination methods on the original spectrum—smooth, standard normal variable transformation (SNV), first derivative (FD), second derivative (SD), smooth + first derivative (Smooth + FD), smooth + second derivative (Smooth + SD), standard normal variable transformation + first derivative (SNV + FD), standard normal variable transformation + second derivative (SNV + SD)—the effects of different methods on the quality of the spectrum and its correlation with SPAD value were compared. The competitive adaptive reweighted sampling algorithm (CARS) was adopted to extract the characteristic wavelength, aiming to reduce data dimensionality and optimize model input. Both BP neural network and RBF neural network prediction models were established, and the model performance under different training functions was compared. The results indicate that after Smooth + FD pretreatment, followed by CARS screening of the characteristic wavelength, the BP neural network model trained using the LBFGS algorithm demonstrated the best performance, with its coefficient of determination (R²) of 0.87 (training set) and 0.85 (validation set), root mean square error (RMSE) of 1.36 (training set) and 1.35 (validation set), and residual prediction deviation (RPD) of 2.81 (training set) and 2.56 (validation set) showing good prediction accuracy and robustness. Research indicates that by combining near-infrared spectroscopy with feature extraction and machine learning methods, the rapid and non-destructive inspection of the grey jujube leaf SPAD value can be achieved, providing reliable technical support for the real-time monitoring of the nutritional status of jujube trees. Full article

Introduction: Three-dimensional (3D) printing technology has rapidly evolved across various fields of medicine and dentistry, particularly in orthodontics. One key application in orthodontics is the fabrication of dental models. Numerous parameters throughout the dental cast fabrication workflow can influence the accuracy of 3D-printed models. This review aims to evaluate the influence of post-processing procedures, specifically post-curing and post-rinsing, on the dimensional accuracy of 3D-printed dental casts. Materials and Methods: An initial data search was conducted using specific keywords across four databases (PubMed, Scopus, Web of Science, and Google Scholar). A secondary search of references and citations was also performed. This systematic review ultimately identified five studies that met the inclusion criteria (in vitro studies and studies referred to post-processing only of 3D-printed models) for further evaluation and analysis, whereas reviews, opinion studies, and papers in languages other than English were excluded. Based on the QUIN tool, all studies were assessed for their risk of bias. Because of the studies’ heterogeneity, a qualitative descriptive synthesis was conducted. Results: All five included studies were in vitro investigations. One study examined the influence of the post-curing process on dimensional accuracy, while the remaining four explored the impact of post-rinsing procedures on both dimensional accuracy and other surface characteristics of 3D-printed dental casts. Conclusions: According to the findings of the included studies, both post-curing and post-rinsing procedures had statistically significant effects on the dimensional accuracy of 3D-printed dental models. Nevertheless, all five studies concluded that the observed deviations remained within clinically acceptable limits, rendering the casts suitable for diagnostic orthodontic purposes or device fabrication. However, further research is needed to reinforce current findings and to enhance our understanding of the optimal post-processing protocols of additively manufactured dental casts. Full article

The freezing method is widely employed in the construction of a vertical shaft in soft soil and water-rich strata. As the construction depth increases, investigating the water–heat–force coupling effects induced by the hydration heat (internal heat source) of concrete is crucial for the safety of the lining structure and its resistance to cracking and seepage. A three-dimensional coupled thermal–hydraulic–mechanical analysis model was developed, incorporating temperature and soil relative saturation as unknown variables based on heat transfer in porous media, unsaturated soil seepage, and frost heave theory. The coefficient type PDE module in COMSOL was used for secondary development to solve the coupling equation, and the on-site temperature and pressure monitoring data of the frozen construction process were compared. This study obtained the model-related parameters and elucidated the evolution mechanism of freeze–thaw and freeze–swelling pressures of a frozen wall under the influence of hydration heat. The resulting model shows that the maximum thaw depth of the frozen wall reaches 0.3576 m after 160 h of pouring, with an error rate of 4.64% compared to actual measurements. The peak temperature of the shaft wall is 73.62 °C, with an error rate of 3.76%. The maximum influence range of hydration heat on the frozen temperature field is 1.763 m. The peak freezing pressure is 4.72 MPa, which exhibits a 5.03% deviation from the actual measurements, thereby confirming the reliability of the resulting model. According to the strength growth pattern of concrete and the freezing pressure bearing requirements, it can provide a theoretical basis for quality control of the lining structure and a safety assessment of the freezing wall. Full article

Popcorn maize (Zea mays everta) exhibits complex morphologies that challenge structural analysis. This study assessed the fidelity of the three-dimensional (3D) reconstruction and printing of four popcorn morphologies, unilateral, bilateral, multilateral, and mushroom, by integrating structured-light 3D scanning and (DIA), which can support the construction of food replicas. Morphometric parameters (projected area, perimeter, Feret diameter, circularity, and roundness) and fractal descriptors (fractal dimension, lacunarity, and entropy) were quantified as the relative ratios of printed/real parameters (P/R) to compare real flakes with their 3D-printed counterparts. Results revealed the lowest mean errors for Feret diameter (6%) and projected area (10%), while deviations in circularity and roundness were more pronounced in mushroom flakes. With respect to the actual mean values of the morphological parameters, real flakes showed slightly larger perimeter values (86 mm for real and 82 mm for printed objects) and a higher fractal dimension (1.36 for real and 1.33 for printed), indicating greater texture irregularity, whereas the projected area remained highly comparable (225 mm² in real/229 mm² in printed). These parameters reinforced that the overall morphological fidelity remained high (P/R = 0.9–1.0), despite localized deviations in circularity and fractal descriptors. Less complex morphologies (unilateral and bilateral) demonstrated higher structural fidelity (P/R = 0.95), whereas multilateral and mushroom types showed greater variability due to surface irregularity. Fractal dimension and lacunarity effectively described textural complexity, highlighting the role of flake geometry and moisture in determining expansion patterns and printing accuracy. Principal Component Analysis confirmed that circularity and fractal indicators are critical descriptors for distinguishing morphological fidelity. Overall, the findings demonstrated that 3D scanning and printing provided reliable physical replicas of irregular food structures as popcorn flakes supporting their application in food engineering. Full article

The turbine mortise is a critical structural feature of turbine disks, and its manufacturing quality directly determines the performance and service life of aircraft engines. With the increasing application of advanced nickel-based superalloys, severe tool wear in conventional mechanical broaching of turbine mortises has emerged as a key limitation, substantially elevating production costs. Electrochemical broaching (ECB), which removes material through anodic dissolution reactions, eliminates tool wear and thus offers low cost and efficiency advantages, making it a promising method for turbine mortise fabrication. In this study, COMSOL Multiphysics 6.2 was employed to simulate the multiphysics field comprising the electric field, flow field, temperature field, bubble ratio, and dynamic mesh and elucidate the evolution of the electric field during the ECB process. ECB experiments of specimens on Inconel 718 were conducted under different feed speeds. On this basis, optimal processing parameters were identified. The results of the mid-position ECB experiments revealed five distinct dissolution states: pre-processing, pre-transition, stable dissolution, post-transition, and post-processing stages. A material dissolution mechanism model for the ECB process was established. Finally, fir-tree turbine mortises were successfully manufactured on Inconel 718 using a self-developed specialized electrochemical machining system at a feed speed of 70 mm/min. The mortise profile demonstrated dimensional deviations of (+16 to −21) μm, with working surface variations maintained within ±5 μm. The machined surfaces exhibited uniform and dense morphology with a surface roughness of Ra 0.275 μm. Three sets of mortise specimens processed under identical parameters showed excellent consistency, presenting a maximum deviation in profile removal thickness of +4.1 μm. The tool cathode was repeatedly reused without any detectable wear. Full article

Due to a lack of investigated materials for the additive manufacturing of multi-use functional parts in bioprocess engineering, this study aimed to evaluate the influence of multiple autoclaving cycles on the properties of a heat-resistant material (xPeek147) printed with vat photopolymerization. Sample bodies were tested regarding their mechanical properties of tensile strength, elongation at break, and Charpy impact, as well as surface properties of roughness and wettability after up to 50 autoclaving cycles (121 °C, 2 bars, 15 min). The tightness was checked after up to 20 cycles, and accuracy was inspected for manufactured benchmark bodies after up to 10 autoclaving cycles. The reported results showed no significant changes in tensile strength, elongation at break and Charpy impact after 20 cycles, but a significant decrease after 50 autoclaving cycles, accompanied by microcracks in the structure. Regarding the surface properties the material retained its hydrophilicity, and the surface roughness was not affected significantly. No changes in tightness occurred, and the benchmark bodies for dimensional changes showed no process-relevant deviations. Through the investigations, a material for the additive manufacturing of multi-use functional parts for bioprocess engineering was identified. Additionally, a testing method for materials with the same intended application was provided. Full article

Floods are among the most frequent and destructive natural hazards worldwide, with increasingly severe socioeconomic consequences due to rapid urbanization, land use changes, and climate variability. While the combination of Geographic Information Systems (GIS) with models such as HEC-RAS has been extensively explored for flood risk management, many existing studies remain limited to one-dimensional (1D) models or use coarse-resolution terrain data, often underestimating flood risk and failing to produce critical multivariate flood characteristics in densely built urban areas. This study applies a two-dimensional (2D) hydraulic modeling framework in HEC-RAS combined with GIS-based spatial analysis, using a high-resolution (1 × 1 m) LiDAR-derived Digital Terrain Model (DTM) and a hybrid mesh refined between 2 × 2 m and 8 × 8 m, with the main contributions represented by the specific application context and methodological choices. A key methodological aspect is the direct integration of synthetic hydrographs with defined exceedance probabilities (10%, 1%, and 0.1%) into the 2D model, thereby reducing the need for extensive hydrological simulations and defining a data-driven approach for resource-constrained environments. The primary novelty is the application of this high-resolution urban modeling framework to a Romanian urban–peri-urban setting, where detailed hydrological observations are scarce. Unlike previous studies in Romania, this approach applies detailed channel and floodplain discretization at high spatial resolution, explicitly incorporating anthropogenic features like buildings and detailed land use roughness for the accurate representation of local hydraulic dynamics. The resulting outputs (inundation extents, depths, and velocities) support risk assessment and spatial planning in the Ungheni locality (Iași County, Romania), providing a practical, transferable workflow adapted to data-scarce regions. Scenario results quantify vulnerability: for the 0.1% exceedance probability scenario (with a calibration accuracy of ±15–30 min deviation for peak flow timing), the flood risk may affect 882 buildings, 42 land parcels, and 13.5 km of infrastructure. This framework contributes to evidence-based decision-making for climate adaptation and disaster risk reduction strategies, improving urban resilience. Full article

This paper investigates the multi-objective siting and sizing problem of a transformer–energy storage deeply integrated system (TES-DIS) that serves as a grid-side common interest entity. This study is motivated by the critical role of energy storage systems in generation–grid–load–storage resource allocation and the superior capability of artificial intelligence algorithms in addressing multi-dimensional, multi-constrained optimization challenges. A multi-objective optimization model is first formulated with dual objectives: minimizing voltage deviation levels and comprehensive economic costs. To overcome the limitations of conventional methods in complex power systems—particularly regarding solution quality and convergence speed—an enhanced Q-learning with hybrid guidance algorithm is proposed. The improved algorithm demonstrates strengthened local search capability and accelerated late-stage convergence performance. Validation using a real-world urban power grid in China confirms the method’s effectiveness. Compared to traditional approaches, the proposed solution achieves optimal TES-DIS planning through autonomous learning, demonstrating (1) 70.73% cost reduction and (2) 89.85% faster computational efficiency. These results verify the method’s capability for intelligent, simplified power system planning with superior optimization performance. Full article

The seepage characteristics and heat transfer efficiency in rough fractures are indispensable for assessing the lifetime and production performance of geothermal reservoirs. In this study, a two-dimensional rough rock fracture model with different secondary roughness is developed using the wavelet analysis method to simulate the coupled flow and heat transfer process under multiscale roughness based on two theories: local thermal equilibrium (LTE) and local thermal nonequilibrium (LTNE). The simulation results show that the primary roughness controls the flow behavior in the main flow zone in the fracture, which determines the overall temperature distribution and large-scale heat transfer trend. Meanwhile, the nonlinear flow behaviors induced by the secondary roughness significantly influence heat transfer performance: the secondary roughness usually leads to the formation of more small-scale eddies near the fracture walls, increasing flow instability, and these changes profoundly affect the local water temperature distribution and heat transfer coefficient in the fracture–matrix system. The eddy aperture and eddy area fraction are proposed for analyzing the effect of nonlinear flow behavior on heat transfer. The eddy area fraction significantly and positively correlates with the overall heat transfer coefficient. Meanwhile, the overall heat transfer coefficient increases by about 3% to 10% for eddy area fractions of 0.3% to 3%. As the eddy aperture increases, fluid mixing is enhanced, leading to a rise in the magnitude of the local heat transfer coefficient. Finally, the roughness characterization was decomposed into primary roughness root mean square and secondary roughness standard deviation, and for the first time, an empirical correlation was established between multiscale roughness, flow velocity, and the overall heat transfer coefficient. Full article

Greenhouse automation has become increasingly important in facility agriculture, yet multi-span glass greenhouses pose both scientific and practical challenges for autonomous mobile robots. Scientifically, solid-state LiDAR is vulnerable to glass-induced reflections, sparse geometric features, and narrow vertical fields of view, all of which undermine Simultaneous Localization and Mapping (SLAM)-based localization and mapping. Practically, large-scale crop production demands accurate inter-row navigation and efficient rail switching to reduce labor intensity and ensure stable operations. To address these challenges, this study presents an integrated localization-navigation framework for mobile robots in multi-span glass greenhouses. In the intralogistics area, the LiDAR Inertial Odometry-Simultaneous Localization and Mapping (LIO-SAM) pipeline was enhanced with reflection filtering, adaptive feature-extraction thresholds, and improved loop-closure detection, generating high-fidelity three-dimensional maps that were converted into two-dimensional occupancy grids for A-Star global path planning and Dynamic Window Approach (DWA) local control. In the cultivation area, where rails intersect with internal corridors, YOLOv8n-based rail-center detection combined with a pure-pursuit controller established a vision-servo framework for lateral rail switching and inter-row navigation. Field experiments demonstrated that the optimized mapping reduced the mean relative error by 15%. At a navigation speed of 0.2 m/s, the robot achieved a mean lateral deviation of 4.12 cm and a heading offset of 1.79°, while the vision-servo rail-switching system improved efficiency by 25.2%. These findings confirm the proposed framework’s accuracy, robustness, and practical applicability, providing strong support for intelligent facility-agriculture operations. Full article

Traditional single-layer water-lubricated rubber or plastic bearings suffer from water film rupture, excessive frictional losses, and insufficient load-carrying capacity, which limit performance and service life in marine propulsion and ocean engineering. To address these issues, this study introduces an innovative laminated bearing consisting of a rubber composite layer and an ultra-high-molecular-weight polyethylene (UHMWPE) layer. A three-dimensional dynamic model based on fluid–structure interaction theory is developed to evaluate the effects of eccentricity, rotational speed, and liner thickness on lubrication pressure, load capacity, deformation, stress–strain behavior, and frictional power consumption. The model also reveals how thickness matching governs load transfer and energy dissipation. Results indicate that eccentricity, speed, and thickness are key determinants of lubrication and structural response. Hydrodynamic pressure and load capacity rise with eccentricity above 0.8 or higher speeds, but frictional losses also intensify. The rubber layer performs optimally at a thickness of 5 mm, while excessive or insufficient thickness leads to stress concentration or reduced buffering. The UHMWPE layer exhibits optimal performance at 5–7 mm, with greater deviations resulting in increased stress and deformation. Proper thickness matching improves pressure distribution, reduces local stresses, and enhances energy dissipation, thereby strengthening bearing stability and durability. Full article