OMAE2005-67311 WATER ENTRY AND EXIT OF A HORIZONTAL CIRCULAR CYLINDER

ABSTRACT This paper describes the fully nonlinear free-surface deformations of initially calm water caused by water-entry and water-exit of a horizontal circular cylinder with both forced and free vertical motions. This has relevance for marine operations as well as for the ability to predict large amplitude motions of floating sea structures. A new numerical method called the CIP (Constrained Interpolation Profile) method is used to solve the problem. In this paper, the circular cylinder and free surface interaction is treated as a multiphase problem, which has liquid (water), gas (air) and solid (circular cylinder) phases. The flow is represented by one set of governing equations, which are solved numerically on a non-uniform, staggered Cartesian grid by a finite difference method. The free surface as well as the body boundary is immersed in the computational domain. The numerical results of the water entry and exit force, the free surface deformation and the vertical motion of the cylinder are compared with experimental results, and favorable agreement is obtained

Culture variation in the average identity extraction: The role of global vs. local processing orientation

Research has shown that observers often spontaneously extract a mean representation from multiple faces/objects in a scene even when this is not required by the task. This phenomenon, now known as ensemble coding, has so far mainly been based on data from Western populations. This study compared East Asian and Western participants in an implicit ensemble-coding task, where the explicit task was to judge whether a test face was present in a briefly exposed set of faces. Although both groups showed a tendency to mistake an average of the presented faces as target, thus confirming the universality of ensemble coding, East Asian participants displayed a higher averaging tendency relative to the Westerners. To further examine how a cultural default can be adapted to global or local processing demand, our second experiment tested the effects of priming global or local processing orientation on ensemble coding via a Navon task procedure. Results revealed a reduced tendency for ensemble coding following the priming of local processing orientation. Together, these results suggest that culture can influence the proneness to ensemble coding, and the default cultural mode is malleable to a temporary processing demand

Generative AI for brain image computing and brain network computing: a review

Recent years have witnessed a significant advancement in brain imaging techniques that offer a non-invasive approach to mapping the structure and function of the brain. Concurrently, generative artificial intelligence (AI) has experienced substantial growth, involving using existing data to create new content with a similar underlying pattern to real-world data. The integration of these two domains, generative AI in neuroimaging, presents a promising avenue for exploring various fields of brain imaging and brain network computing, particularly in the areas of extracting spatiotemporal brain features and reconstructing the topological connectivity of brain networks. Therefore, this study reviewed the advanced models, tasks, challenges, and prospects of brain imaging and brain network computing techniques and intends to provide a comprehensive picture of current generative AI techniques in brain imaging. This review is focused on novel methodological approaches and applications of related new methods. It discussed fundamental theories and algorithms of four classic generative models and provided a systematic survey and categorization of tasks, including co-registration, super-resolution, enhancement, classification, segmentation, cross-modality, brain network analysis, and brain decoding. This paper also highlighted the challenges and future directions of the latest work with the expectation that future research can be beneficial

Classification of 5-HT1A Receptor Ligands on the Basis of Their Binding Affinities by Using PSO-Adaboost-SVM

In the present work, the support vector machine (SVM) and Adaboost-SVM have been used to develop a classification model as a potential screening mechanism for a novel series of 5-HT1A selective ligands. Each compound is represented by calculated structural descriptors that encode topological features. The particle swarm optimization (PSO) and the stepwise multiple linear regression (Stepwise-MLR) methods have been used to search descriptor space and select the descriptors which are responsible for the inhibitory activity of these compounds. The model containing seven descriptors found by Adaboost-SVM, has showed better predictive capability than the other models. The total accuracy in prediction for the training and test set is 100.0% and 95.0% for PSO-Adaboost-SVM, 99.1% and 92.5% for PSO-SVM, 99.1% and 82.5% for Stepwise-MLR-Adaboost-SVM, 99.1% and 77.5% for Stepwise-MLR-SVM, respectively. The results indicate that Adaboost-SVM can be used as a useful modeling tool for QSAR studies