Search Results (9)

The article aims to design a calm water resistance predictor based on Machine Learning (ML) Tools and develop a systematic series for battery-driven catamaran hullforms. Additionally, employing a machine learning predictor for design optimization through the utilization of a Genetic Algorithm (GA) in an expedited manner. Regression Trees (RTs), Support Vector Machines (SVMs), and Artificial Neural Network (ANN) regression models are applied for dataset training. A hullform optimization was implemented for various catamarans, including dimensional and hull coefficient parameters based on resistance, structural weight reduction, and battery performance improvement. Design distribution based on Lackenby transformation fulfills all of the design space, and sequentially, a novel self-blending method reconstructs new hullforms based on two parents blending. Finally, a machine learning approach was conducted on the generated data of the case study. This study shows that the ANN algorithm correlates well with the measured resistance. Accordingly, by choosing any new design based on owner requirements, GA optimization obtained the final optimum design by using an ML fast resistance calculator. The optimization process was conducted on a 40 m passenger catamaran case study that achieved a 9.5% cost function improvement. Results show that incorporating the ML tool into the GA optimization process accelerates the ship design process. Full article

Employing multi-stage fracturing technology in horizontally accessed wells is a well-known way to successfully develop shale reservoirs. The interaction between natural fractures and hydraulic fractures has a significant impact on the fracturing effect. In this study, a coupled model of rock deformation and fluid flow was established using the cohesive zone method to simulate the propagation of hydraulic fractures under the synergistic effect of natural fractures and wellbore interference. The influence of in situ stress, fracture spacing, the number of fracture clusters, and the fracturing methods on the formation of fracture networks was analyzed. Studies on the fracture morphology and connectivity of fracture networks show that when the in situ stress difference is small, multiple fractures can easily form, and when the in situ stress difference is large, they can easily gather into a single fracture. An excessive reduction in fracture spacing may impede the optimal propagation and interconnection of hydraulic fractures. The findings reveal that augmenting the fracture spacing from 5 m to 8 m results in a significant 15.59% increase in the overall extent of fracture propagation. Moreover, it also adds to the complexity of the fracture network. Increasing the number of hydraulic fracturing clusters can improve the fracture length and fracture propagation complexity. When the number of fracturing clusters increased from two clusters to five clusters, the maximum fracture propagation width increased by 25.23%. Comparing sequential fracturing and simultaneous fracturing, the results show that simultaneous fracturing can form a more complex fracture network with better connectivity, which is conducive to increasing oil and gas production. The obtained results can provide a reference for horizontal well fracturing designs of shale reservoirs. Full article

Hull-form optimization is a complex engineering problem. Owing to the several numerical simulations and complex design-performance spaces, hull-form optimization is considered an inefficient process, which makes determining the global optimum difficult. This study used rough set theory (RST) to acquire knowledge and reduce the design space for hull-form optimization. Furthermore, we studied one of the hull-form optimization problems by practically applying RST to the appropriate number of sampling points. To solve this problem, we proposed the RST-based sequential design-space reduction (SDSR) method that uses interval theory to calculate subspace intersections and unions, as well as test calculations to choose an appropriate stopping criterion. Finally, SDSR was used to optimize a KRISO container ship to minimize the wave-making resistance. The results were compared to those of direct optimization and one-time design-space reduction, thus proving the feasibility of this method. Full article

The administration of viral vectored vaccines remains one of the most effective ways to respond to the ongoing novel coronavirus disease 2019 (COVID-19) pandemic. However, pre-existing immunity to the viral vector hinders its potency, resulting in a limited choice of viral vectors. Moreover, the basic batch mode of manufacturing vectored vaccines does not allow one to cost-effectively meet the global demand for billions of doses per year. To date, the exposure of humans to VSV infection has been limited. Therefore, a recombinant vesicular stomatitis virus (rVSV), which expresses the spike protein of SARS-CoV-2, was selected as the vector. To determine the operating upstream process conditions for the most effective production of an rVSV-SARS-CoV-2 candidate vaccine, a set of critical process parameters was evaluated in an Ambr 250 modular system, whereas in the downstream process, a streamlined process that included DNase treatment, clarification, and a membrane-based anion exchange chromatography was developed. The design of the experiment was performed with the aim to obtain the optimal conditions for the chromatography step. Additionally, a continuous mode manufacturing process integrating upstream and downstream steps was evaluated. rVSV-SARS-CoV-2 was continuously harvested from the perfusion bioreactor and purified by membrane chromatography in three columns that were operated sequentially under a counter-current mode. Compared with the batch mode, the continuous mode of operation had a 2.55-fold increase in space–time yield and a reduction in the processing time by half. The integrated continuous manufacturing process provides a reference for the efficient production of other viral vectored vaccines. Full article

Lane-level route planning is a critical issue for a lane-level navigation system for autonomous vehicles. Current route-planning methods mainly focus on the road level and applying them directly to search for lane-level routes results in a reduction in search efficiency. In addition, previously developed lane-level methods lack consideration for vehicle characteristics and adaptability to multiple road network structures. To solve this issue, this study proposes an accelerated and refined lane-level route-planning algorithm based on a new lane-level road network model. First, five sub-layers are designed to refine the internal structure of the divided road and intersection areas so that the model can express multiple variations in road network structures. Then, a multi-level route-planning algorithm is designed for sequential planning at the road level, lane group level, lane section level, and lane level to reduce the search space and significantly improve routing efficiency. Last, an optimal lane determination algorithm considering traffic rules, vehicle characteristics, and optimization objectives is developed at the lane level to find the optimal lanes on roads with different configurations, including those with a constant or variable number of lanes while satisfying traffic rules and vehicle characteristics. Tests were performed on simulated road networks and a real road network. The results demonstrate the algorithm’s better adaptability to changing road network structures and vehicle characteristics compared with past hierarchical route planning, and its higher efficiency compared with direct route planning, past hierarchical route planning, and the Apollo route-planning method, which can better support autonomous vehicle navigation. Full article

Piezoelectric surgical instruments with various mini-sized tips and cutting technology offer a precise and thin cutting line that could allow the wider use of periodontal osseous wall swaging. This randomized controlled trial was designed to investigate the use of a minimally invasive piezo knife to harvest vascularized interseptal bone pedicles in treating intra-bony defects. Sixteen non-smoking patients (mean age 39.6 ± 3.9) with severe chronic periodontitis were randomly assigned into one of two groups (N = 8). The Group 1 (control) patients were treated by bone substitute grafting of the intra-bony defect, whereas the Group 2 patients were treated by intra-bony defect osseous wall swaging (OWS) combined with xenograft filling of the space created by bone tilting. In both groups, the root surfaces were treated with a neutral 24% EDTA gel followed by saline irrigation. Clinical and radiographic measurements were obtained at baseline and 6 months after surgery. The sites treated with osseous wall swaging showed a statistically significant probing-depth reduction and increase in clinical attachment compared with those of the Group 1 patients. The defect base level was significantly reduced for the OWS group compared to that of the Group 1 control. By contrast, the crestal bone level was significantly higher in the OWS group compared to Group 1. The crestal interseptal bone width was significantly higher in Group 2 at 6 months compared to the baseline value and to that of Group 1 (<0.001). The osseous wall swaging effectively improved the clinical hard- and soft-tissue parameters. The use of mini inserts piezo-cutting, sequential bone expanders for osseous wall redirection, and root surface EDTA etching appears to be a reliable approach that could allow the use of OWS at any interproximal dimension. Full article

We describe a new software package for the data reduction of single crystal neutron diffraction using large 2D detectors. The software consists of a graphical user interface from which the user can visualize, interact with and process the data. The data reduction is achieved by sequentially executing a series of programs designed for performing the following tasks: peak detection, indexing, refinement of the orientation matrix and motor offsets, and integration. The graphical tools of the software allow visualization of and interaction with the data in two and three dimensions, both in direct and reciprocal spaces. They make it easy to validate the different steps of the data reduction and will be of great help in the treatment of complex problems involving incommensurate structures, twins or diffuse scattering. Full article

Analyzing the surface and bedrock locations in radar imagery enables the computation of ice sheet thickness, which is important for the study of ice sheets, their volume and how they may contribute to global climate change. However, the traditional handcrafted methods cannot quickly provide quantitative, objective and reliable extraction of information from radargrams. Most traditional handcrafted methods, designed to detect ice-surface and ice-bed layers from ice sheet radargrams, require complex human involvement and are difficult to apply to large datasets, while deep learning methods can obtain better results in a generalized way. In this study, an end-to-end multi-scale attention network (MsANet) is proposed to realize the estimation and reconstruction of layers in sequences of ice sheet radar tomographic images. First, we use an improved 3D convolutional network, C3D-M, whose first full connection layer is replaced by a convolution unit to better maintain the spatial relativity of ice layer features, as the backbone. Then, an adjustable multi-scale module uses different scale filters to learn scale information to enhance the feature extraction capabilities of the network. Finally, an attention module extended to 3D space removes a redundant bottleneck unit to better fuse and refine ice layer features. Radar sequential images collected by the Center of Remote Sensing of Ice Sheets in 2014 are used as training and testing data. Compared with state-of-the-art deep learning methods, the MsANet shows a 10% reduction (2.14 pixels) on the measurement of average mean absolute column-wise error for detecting the ice-surface and ice-bottom layers, runs faster and uses approximately 12 million fewer parameters. Full article

Land cover classification plays a pivotal role in Earth resource management. In the past, synthetic aperture radar (SAR) had been extensively studied for classification. However, limited work has been done on multi-temporal datasets owing to the lack of data availability and computational power. As Earth observation (EO) becomes more and more imperative, it becomes essential to exploit the information embedded in multi-temporal datasets. In this paper, we present a framework for SAR pixel labeling. Specifically, we exploit spatio-temporal information for pixel labeling. The proposed scheme includes four steps: (1) extraction of spatio-temporal observations; (2) feature computation; (3) feature reduction and (4) pixel labeling. First, an adaptive approach is applied to the data cube to extract spatio-temporal observations in both coherent and incoherent domains. Second, features in distinct domains are designed and computed to boost information content embedded in the multi-temporal datasets. Third, sequential feature selection is utilized for selecting the most discriminative features among the entire feature space. Last, the discriminative classifier is used to label the class of each pixel. By integrating pixel-/object-based processing techniques, spatial/temporal observations and coherent/incoherent data attributes, the proposed method explores diverse observations to solve complex labeling problems. In the experiments, we apply the proposed method on 64 TanDEM-X images and 70 COSMO-SkyMed high-resolution images, respectively. Both experiments reveal high accuracies for multi-class labeling. The proposed technique, therefore, provides a new solution for classifying multi-temporal single-polarized datasets. Full article