Search Results (57,043)

In this study, a series of carrier-phase direct numerical simulations are conducted on spherical expanding premixed hydrogen/air flames with liquid water addition. An Eulerian–Lagrangian approach with two-way coupling is employed to describe the liquid–gas interaction. The impacts of preferential diffusion, the equivalence ratio, water loading, and the initial diameter of the water droplets are examined and analyzed in terms of flame evolution. It is observed that liquid water has the potential to influence flame propagation characteristics by reducing the total burning rate, flame area, and burning rate per unit area, attributed to flame cooling effects. However, these effects become discernible only under conditions where water evaporation is sufficiently intense. For the conditions investigated, the influence of preferential diffusion on flame evolution is found to be more significant than the interaction with liquid water. The results suggest that due to the slow evaporation rate of water, which is a result of its high latent heat of evaporation, the water droplets do not disturb the initial flame kernel growth significantly. This has implications for water injection concepts in internal combustion engines and for explosion mitigation. Full article

Eye diseases are defined as disorders or diseases that damage the tissue and related parts of the eyes. They appear in various types and can be either minor, meaning that they do not last long, or permanent blindness. Cataracts, glaucoma, and diabetic retinopathy are all eye illnesses that can cause vision loss if not discovered and treated early on. Automated classification of these diseases from fundus images can empower quicker diagnoses and interventions. Our research aims to create a robust model, BayeSVM500, for eye disease classification to enhance medical technology and improve patient outcomes. In this study, we develop models to classify images accurately. We start by preprocessing fundus images using contrast enhancement, normalization, and resizing. We then leverage several state-of-the-art deep convolutional neural network pre-trained models, including VGG16, VGG19, ResNet50, EfficientNet, and DenseNet, to extract deep features. To reduce feature dimensionality, we employ techniques such as principal component analysis, feature agglomeration, correlation analysis, variance thresholding, and feature importance rankings. Using these refined features, we train various traditional machine learning models as well as ensemble methods. Our best model, named BayeSVM500, is a Support Vector Machine classifier trained on EfficientNet features reduced to 500 dimensions via PCA, achieving 93.65 ± 1.05% accuracy. Bayesian hyperparameter optimization further improved performance to 95.33 ± 0.60%. Through comprehensive feature engineering and model optimization, we demonstrate highly accurate eye disease classification from fundus images, comparable to or superior to previous benchmarks. Full article

Northern Goshawk Optimization (NGO) is an efficient optimization algorithm, but it has the drawbacks of easily falling into local optima and slow convergence. Aiming at these drawbacks, an improved NGO algorithm named the Multi-Strategy Improved Northern Goshawk Optimization (MSINGO) algorithm was proposed by adding the cubic mapping strategy, a novel weighted stochastic difference mutation strategy, and weighted sine and cosine optimization strategy to the original NGO. To verify the performance of MSINGO, a set of comparative experiments were performed with five highly cited and six recently proposed metaheuristic algorithms on the CEC2017 test functions. Comparative experimental results show that in the vast majority of cases, MSINGO’s exploitation ability, exploration ability, local optimal avoidance ability, and scalability are superior to those of competitive algorithms. Finally, six real world engineering problems demonstrated the merits and potential of MSINGO. Full article

Functionally Graded Materials (FGMs) can outperform their homogeneous counterparts. Advances in digitalization technologies, mainly additive manufacturing, have enabled the synthesis of materials with tailored properties and functionalities. Joining dissimilar metals to attain compositional grading is a relatively unexplored research area and holds great promise for engineering applications. Metallurgical challenges may arise; thus, a theoretical critical analysis is presented in this paper. A multidisciplinary methodology is proposed here to unify optimal design, multi-feed Wire-Arc Additive Manufacturing (WAAM), and image-based characterization methods to create structure-specific oriented FGM parts. Topology optimization is used to design FGMs. A beam under pure bending is used to explore the layer-wise FGM concept, which is also analytically validated. The challenges, limitations, and role of WAAM in creating FGM parts are discussed, along with the importance of numerical validation using full-field deformation data. As a result, a conceptual FGM engineering workflow is proposed at this stage, enabling digital data conversion regarding geometry and compositional grading. This is a step forward in processing in silico data, with a view to experimentally producing parts in future. An optimized FGM beam, revealing an optimal layout and a property gradient from iron to copper along the build direction (bottom–up) that significantly reduces the normal pure bending stresses (by 26%), is used as a case study to validate the proposed digital workflow. Full article

Restoring the structures and functions of tissues along with organs in human bodies is a topic gathering attention nowadays. These issues are widely discussed in the context of regenerative medicine. Excipients/delivery systems play a key role in this topic, guaranteeing a positive impact on the effectiveness of the drugs or therapeutic substances supplied. Advances in materials engineering, particularly in the development of hydrogel biomaterials, have influenced the idea of creating an innovative material that could serve as a carrier for active substances while ensuring biocompatibility and meeting all the stringent requirements imposed on medical materials. This work presents the preparation of a natural polymeric material based on pullulan modified with silymarin, which belongs to the group of flavonoids and derives from a plant called Silybum marianum. Under UV light, matrices with a previously prepared composition were crosslinked. Before proceeding to the next stage of the research, the purity of the composition of the matrices was checked using Fourier-transform infrared (FT-IR) spectroscopy. Incubation tests lasting 19 days were carried out using incubation fluids such as simulated body fluid (SBF), Ringer’s solution, and artificial saliva. Changes in pH, electrolytic conductivity, and weight were observed and then used to determine the sorption capacity. During incubation, SBF proved to be the most stable fluid, with a pH level of 7.6–7.8. Sorption tests showed a high sorption capacity of samples incubated in both Ringer’s solution and artificial saliva (approximately 350%) and SBF (approximately 300%). After incubation, the surface morphology was analyzed using an optical microscope for samples demonstrating the greatest changes over time. The active substance, silymarin, was released using a water bath, and then the antioxidant capacity was determined using the Folin–Ciocâlteu test. The tests carried out proved that the material produced is active and harmless, which was shown by the incubation analysis. The continuous release of the active ingredient increases the biological value of the biomaterial. The material requires further research, including a more detailed assessment of its balance; however, it demonstrates promising potential for further experiments. Full article

Distributed Acoustic Sensing (DAS), widely adopted in hydraulic fracturing monitoring, continuously measures sound from perforation holes due to fluid flow through the perforation holes during fracturing treatment. DAS has the potential to monitor perforation Tulsa, OK 74136erosion, a phenomenon of increasing perforation size due to sand (referred to as proppant) injection during treatment. Because the sound generated by fluid flow at a perforation hole is negatively related to the perforation diameter, by detecting the decay of the DAS signal, the perforation erosion level can be estimated, which is critical information for fracture design. We used a Computation Fluid Dynamics (CFD) acoustic simulator to calculate the acoustic pressure induced by turbulence inside a wellbore and investigated the relationship between the acoustic response from fluid flow through a perforation and the perforation size by running the simulator for various perforation diameters and flow rates. The results show that if the perforation size is constant, the plot between the calculated sound pressure level and the logarithm of flow rate follows a straight line relationship. However, with different perforation sizes, the intercept of the linear relationship changes, reducing the sound pressure level. Lighthill’s power law indicates that the change in intercept corresponds to the logarithm of the ratio of the increased diameter to the original diameter. The reduction in sound pressure level observed in the CFD simulation correlates with the reduction in the DAS signal in field data. The findings of this study help to evaluate perforation diameter growth using DAS and interpret fluid distribution in fracture stimulation. Full article

With the escalating demand for advanced and eco-friendly processing technologies in mining engineering, the potential applications of microwave heating technology in the treatment of cement tailings backfill (CTB) are expanding significantly. This research comprehensively investigates the mechanisms through which microwave irradiation duration and power influence the mechanical properties of CTB with varying concentrations and cement-to-sand ratios. The aim is to reveal the influencing patterns through experimental methods, providing scientific evidence for optimizing CTB treatment processes. This paper conducted microwave heating tests, uniaxial compression tests, and SEM-EDS tests on CTB. The research results indicate that heating time and power significantly enhance the early strength of CTB, with a more pronounced effect on CTB with higher concentrations and higher cement–sand ratios. When the heating time is 7 min and the heating power is 340 W, the cement hydration reaction is maximally promoted, thereby increasing the density and strength growth rate of CTB. However, excessively long heating time or overly high heating power may cause microcracks or thermal stress concentration within the CTB, adversely affecting the strength growth rate of CTB. Optimal thermal exposure duration and microwave power settings facilitate the activation of cementitious materials and the nucleation of calcium-silicate-hydrate (C-S-H) phases, thereby accelerating the compressive strength evolution of cemented tailings backfill (CTB). The outcomes of this research offer valuable insights into the deployment of microwave heating methodologies in underground mine backfilling, which are pivotal for augmenting the economic viability and environmental sustainability of mining operations. Full article

The interaction between the natural environmental and socioeconomic factors is crucial for assessing the dynamics of plateau ecosystems. Therefore, the remote sensing ecological index (RSEI) and CatBoost-SHAP model were employed to investigate changes in the ecological quality and their driving factors in the Diqing Tibetan Autonomous Prefecture, China, from 2001 to 2021. The results showed an increase from 0.44 in 2001 to 0.71 in 2021 in the average RSEI for the Diqing Prefecture, indicating an overall upward trend in the ecological quality. Spatial analysis shows the percentage of the area covered by different levels of RSEI and their temporal changes. The results revealed that “good” ecological quality accounted for the largest proportion of the study area, at 42.77%, followed by “moderate” at 21.93%, and “excellent” at 16.62%. “Fair” quality areas accounted for 16.11% and “poor” quality areas only 2.57%. The study of ecological and socioeconomic drivers based on the CatBoost-SHAP framework also indicated that natural climate factors have a greater impact on ecological quality than socioeconomic factors; however, this effect differed significantly with altitude. The findings suggest that, in addition to strengthening climate monitoring, further advancements in ecological engineering are required to ensure the sustainable development of the ecosystem and the continuous improvement of the environmental quality in the Diqing Prefecture. Full article

In offshore pile engineering, the installation of jacked piles generates compaction effects within soil, thus further affecting previously installed adjacent piles. This study proposes a three-dimensional numerical model for pile group installation, soil consolidation, and loading analysis. Subsequently, the effect of pile spacing and pile length-to-diameter ratio on the deformation, internal forces, and vertical bearing capacity of adjacent piles are investigated. The results indicate that with an increase in pile center distance, the peak lateral displacement of the adjacent piles decreases, whereas the peak vertical displacement increases. As the pile length-to-diameter ratio increases, the peak vertical and lateral displacements of the adjacent piles are enhanced. In addition, the peak axial force of the adjacent piles initially decreases and then increases with the penetration depth of the subsequent pile, whereas the peak bending moment initially increases and then decreases. The vertical bearing capacity of the subsequent pile is significantly superior to that of the adjacent piles. Therefore, the effects of pile installation on adjacent piles should be included in pile engineering. The impact of the subsequent pile installation on the bearing capacity of adjacent piles can be significantly reduced by increasing the pile center distance and pile length-to-diameter ratio. The findings provide useful guidance for pile group engineering. Full article

Composite track-etched membranes (CTeMs) emerged as a versatile and high-performance class of materials, combining the precise pore structures of traditional track-etched membranes (TeMs) with the enhanced functionalities of integrated nanomaterials. This review provides a comprehensive overview of the synthesis, functionalization, and applications of CTeMs. By incorporating functional phases such as metal nanoparticles and conductive nanostructures, CTeMs exhibit improved performance in various domains. In environmental remediation, CTeMs effectively capture and decompose pollutants, offering both separation and detoxification. In sensor technology, they have the potential to provide high sensitivity and selectivity, essential for accurate detection in medical and environmental applications. For energy storage, CTeMs may be promising in enhancing ion transport, flexibility, and mechanical stability, addressing key issues in battery and supercapacitor performance. Biomedical applications may benefit from the versality of CTeMs, potentially supporting advanced drug delivery systems and tissue engineering scaffolds. Despite their numerous advantages, challenges remain in the fabrication and scalability of CTeMs, requiring sophisticated techniques and meticulous optimization. Future research directions include the development of cost-effective production methods and the exploration of new materials to further enhance the capabilities of CTeMs. This review underscores the transformative potential of CTeMs across various applications and highlights the need for continued innovation to fully realize their benefits. Full article

Populus euphratica is a unique constructive tree species within riparian desert areas that is essential for maintaining oasis ecosystem stability. The Tarim River Basin contains the most densely distributed population of P. euphratica forests in the world, and obtaining accurate distribution data in the mainstream of the Tarim River would provide important support for its protection and restoration. We propose a new method for automatically extracting P. euphratica using Sentinel-1 and 2 and Landsat-8 images based on the Google Earth Engine cloud platform and the random forest algorithm. A mask of the potential distribution area of P. euphratica was created based on prior knowledge to save computational resources. The NDVI (Normalized Difference Vegetation Index) time series was then reconstructed using the preferred filtering method to obtain phenological parameter features, and the random forest model was input by combining the phenological parameter, spectral index, textural, and backscattering features. An active learning method was employed to optimize the model and obtain the best model for extracting P. euphratica. Finally, the map of natural P. euphratica forests with a resolution of 10 m in the mainstream of the Tarim River was obtained. The overall accuracy, producer’s accuracy, user’s accuracy, kappa coefficient, and F1-score of the map were 0.96, 0.98, 0.95, 0.93, and 0.96, respectively. The comparison experiments showed that simultaneously adding backscattering and textural features improved the P. euphratica extraction accuracy, while textural features alone resulted in a poor extraction effect. The method developed in this study fully considered the prior and posteriori information and determined the feature set suitable for the P. euphratica identification task, which can be used to quickly obtain accurate large-area distribution data of P. euphratica. The method can also provide a reference for identifying other typical desert vegetation. Full article

To address the problem of predicting machining quality for critical features in the manufacturing process of mechanical products, a method that combines information entropy and XGBoost (version 2.1.1) hyperparameter optimization is proposed. Initially, machining data of mechanical products are analyzed based on information entropy theory to identify critical quality characteristics. Subsequently, a quality prediction model for these critical features is established using the XGBoost machine learning framework. The model’s hyperparameters are then optimized through Bayesian optimization. This method is applied as a case study to a medium-speed marine diesel engine piston. After the critical quality characteristics in the machining process are identified, the machining quality of these vital characteristics is predicted, and the results are compared with those obtained from a machine learning model without hyperparameter optimization. The findings demonstrate that the proposed method effectively predicts the machining quality of mechanical products. Full article

Brick warehouses represent interdisciplinary heritage sites developed by social, cultural, and economic impacts. This study aimed to connect warehouse details and GIS maps in augmented reality (AR) based on the former Camphor Refinery Workshop Warehouse. AR was applied as an innovation interface to communicate the differences between construction details, providing a feasible on-site solution for articulating historical brick engineering technology. A complex warehouse cluster was georeferenced by the AR models of brick details. The map was assisted by a smartphone-based comparison of the details of adjacent warehouses. Sixty AR models of warehouse details exemplified the active and sustainable preservation of the historical artifacts. The side-by-side allocation of warehouse details in AR facilitated cross-comparisons of construction differences. We found that a second reconstructed result integrated AR and reality in a novel manner based on the use of a smartphone AR. GIS and AR facilitated a management effort using webpages and cloud access from a remote site. The vocabulary of building details can be enriched and better presented in AR. Full article

The enormous energy carried by discharged water poses a serious threat to the Piano Key Weir (PKW) and its downstream hydraulic structures. However, previous research on energy dissipation in PKWs has mainly focused downstream effects, and the research methods have been largely limited to physical model experiments. To deeply investigate the discharge capacity and hydraulic characteristics of PKW, this study established a PKW model with universally applicable geometric parameters. By combining physical model experiments and numerical simulations, the flow pattern of the PKW, the discharge at the overflow edges, and the variation in the energy dissipation were revealed for different water heads. The results showed that the discharge of the side wall constitutes the majority of the total discharge at low water heads, resulting in a relatively high overall discharge efficiency. As the water head increases, the proportion of discharge from the inlet and outlet keys increases, while the proportion from the side wall decreases. This change results in less discharge from the side wall and a consequent reduction in the overall discharge efficiency. The PKW exhibits superior energy dissipation efficiency under low water heads. However, this efficiency exhibits an inverse relationship with an increasing water head. The overall energy dissipation efficiency can reach 40% to 70%. Additionally, the collision of the water flows inside the outlet chamber and the mixing of the overflow jet play a primary role in energy dissipation. The findings of this study have significant implications for hydraulic engineering construction and PKW operational safety. Full article

Additive manufacturing (AM) methods have been gaining momentum because they provide vast design and fabrication possibilities, increasing the accessibility of state-of-the-art hardware through recent developments in user-friendly computer-aided drawing/engineering/manufacturing (CAD/CAE/CAM) tools. However, in comparison to the conventional manufacturing methods, AM processes have some disadvantages, including the machining precision and fabrication process times. The first issue has been mostly resolved through the recent advances in manufacturing hardware, sensors, and controller systems. However, the latter has been widely investigated by researchers with different toolpath planning perspectives. As a contribution to these investigations, the present study proposes a toolpath planning method for AM, which aims to provide highly continuous yet distance-optimized solutions. The approach is based on the utilization of the signed distance field (SDF), clustering, and minimization of toolpath distances among cluster centroids. The method was tested on various geometries with simple closed curves to complex geometries with holes, which provides effective toolpaths, e.g., with relative distance reduction percentages up to 16.5% in comparison to conventional rectilinear infill patterns. Full article