Consumer Attitudes and Purchase Intentions of Cruises in China

This study examined the impact of consumers’ attitudes towards cruises on their purchase intention of cruises. A sample of 229 past outbound travelers in China were surveyed via an online self-administered survey sent by a Chinese online survey platform named ’So Jump’. The survey used a two-part statement evaluation format to measure cruise purchase intentions and the factors that impact the intentions. Respondents were asked to rate their level of agreement, the important of the sub-factors, and the likelihood to participate in certain activities on a seven-point Likert scale. Use the theory of planned behavior as the base, the three factors of cruise purchase intentions proposed prior to conducting the study were; attitude, subjective norm, and perceived behavioral control. Factor analysis revealed that one of the factors was not in the proposed construct. The new factor was named ‘personal concern’. It was found that both attitude and perceived behavioral control have marginally or significant effect on the willingness and the likelihood of cruise taking. Significant differences in attitudes and cruise purchase intentions were found among different age, marital status, employment status, education level and residence groups

Target Identification Using Dictionary Matching of Generalized Polarization Tensors

The aim of this paper is to provide a fast and efficient procedure for (real-time) target identification in imaging based on matching on a dictionary of precomputed generalized polarization tensors (GPTs). The approach is based on some important properties of the GPTs and new invariants. A new shape representation is given and numerically tested in the presence of measurement noise. The stability and resolution of the proposed identification algorithm is numerically quantified.Comment: Keywords: generalized polarization tensors, target identification, shape representation, stability analysis. Submitted to Foundations of Computational Mathematic

Facilitating dynamic web service composition with fine-granularity context management

Context is an important factor for the success of dynamic service composition. Although many contextbased AI or workflow approaches have been proposed to support dynamic service composition, there is still an unaddressed issue of the support of fine-granularity context management. In this paper, we propose a granularity-based context model together with an approach to supporting the intelligent context-aware service composing problem. The corresponding case study is provided to show the validity of our approach.<br /

Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RL

Offline reinforcement learning (RL) offers an appealing approach to real-world tasks by learning policies from pre-collected datasets without interacting with the environment. However, the performance of existing offline RL algorithms heavily depends on the scale and state-action space coverage of datasets. Real-world data collection is often expensive and uncontrollable, leading to small and narrowly covered datasets and posing significant challenges for practical deployments of offline RL. In this paper, we provide a new insight that leveraging the fundamental symmetry of system dynamics can substantially enhance offline RL performance under small datasets. Specifically, we propose a Time-reversal symmetry (T-symmetry) enforced Dynamics Model (TDM), which establishes consistency between a pair of forward and reverse latent dynamics. TDM provides both well-behaved representations for small datasets and a new reliability measure for OOD samples based on compliance with the T-symmetry. These can be readily used to construct a new offline RL algorithm (TSRL) with less conservative policy constraints and a reliable latent space data augmentation procedure. Based on extensive experiments, we find TSRL achieves great performance on small benchmark datasets with as few as 1% of the original samples, which significantly outperforms the recent offline RL algorithms in terms of data efficiency and generalizability.Comment: The first two authors contributed equall

Quantitative Expression of Outlet Deviation Angle of Turbomachine Stator Based on Equivalent Moment of Momentum Principle

In order to express outlet deviation angle of turbomachine stator, outlet deviation angle dominantly expressed by fluid velocity distribution was derived based on equivalent moment of momentum under the condition of certain CFD simulation design parameters. On this basis, response surface function of outlet deviation angle was constructed by CFD simulation data of orthogonal experiment. The validity of the response surface function was proved by CFD simulation data of confirmatory models. An effective method is provided for calculating outlet deviation angle of turbomachine stator

The mechanism of Fe-rich intermetallic compound formation and growth on inoculants revealed by electron backscattered diffraction and X-ray imaging

Fe-rich intermetallics affect critically the mechanical properties and recyclability of aluminium alloys. Increasing effort has been spent on the inoculation of these intermetallics, hoping to promote a finer distribution. Recently Al-5Ti-1B (wt.%), originally developed to refine -Al, has been shown to refine Al13Fe4, an intermetallic phase present in a variety of Al alloys. However, mechanisms of the formation and growth of the intermetallics on the inoculants are unclear. In this paper, Ti is added to Fe-containing Al alloys to produce a large number of potent Al3Ti particles, the active inoculant in Al-5Ti-1B. We use a combination of electron backscattered diffraction, in situ synchrotron X-ray radiography and post-solidification X-ray computed tomography to investigate the formation and growth of primary Al13Fe4 on Al3Ti inoculants, first in a model Al-Fe alloy, with key insights then confirmed in a high Fe-containing, recycled 6xxx alloy. Crystallographic orientation relationships between Al13Fe4 and Al3Ti are analysed comprehensively, and the formation and growth dynamics of Al13Fe4 on Al3Ti is also unveiled. A strong link is revealed between the formation of Al13Fe4 on Al3Ti and a twinning-related pseudo-symmetry of Al13Fe4. Finally, a potential strategy to refine both intermetallics and -Al in recycled alloys with elevated Fe concentration is proposed

PRED_PPI: a server for predicting protein-protein interactions based on sequence data with probability assignment

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions (PPIs) are crucial for almost all cellular processes, including metabolic cycles, DNA transcription and replication, and signaling cascades. Given the importance of PPIs, several methods have been developed to detect them. Since the experimental methods are time-consuming and expensive, developing computational methods for effectively identifying PPIs is of great practical significance.</p> <p>Findings</p> <p>Most previous methods were developed for predicting PPIs in only one species, and do not account for probability estimations. In this work, a relatively comprehensive prediction system was developed, based on a support vector machine (SVM), for predicting PPIs in five organisms, specifically humans, yeast, <it>Drosophila</it>, <it>Escherichia coli</it>, and <it>Caenorhabditis elegans</it>. This PPI predictor includes the probability of its prediction in the output, so it can be used to assess the confidence of each SVM prediction by the probability assignment. Using a probability of 0.5 as the threshold for assigning class labels, the method had an average accuracy for detecting protein interactions of 90.67% for humans, 88.99% for yeast, 90.09% for <it>Drosophila</it>, 92.73% for <it>E. coli</it>, and 97.51% for <it>C. elegans</it>. Moreover, among the correctly predicted pairs, more than 80% were predicted with a high probability of ≥0.8, indicating that this tool could predict novel PPIs with high confidence.</p> <p>Conclusions</p> <p>Based on this work, a web-based system, Pred_PPI, was constructed for predicting PPIs from the five organisms. Users can predict novel PPIs and obtain a probability value about the prediction using this tool. Pred_PPI is freely available at <url>http://cic.scu.edu.cn/bioinformatics/predict_ppi/default.html</url>.</p