An Empirical Research On The Antecedent-Consequence Integrating Model Of Recreationist-Environment Fit: A Case Study On Sport Tourists In Guaninting Waters Of Penghu

The purpose of this research is to verify the validity of an antecedent-consequence integrating model of recreationist-environment fit (R-E fit). This study selected the marine sport tourists in the Guaninting waters of Penghu as the subjects. The research used a questionnaire survey with 300 questionnaires handed out and returned. Of these, 253 were valid and 47 were invalid; therefore, the effective response rate was 84.3%. Then the associated data were collected for descriptive statistical analysis and structural equation modeling analysis using statistical software SPSS20.0 and Smart PLS 2.0. The research results reveal the following conclusions: (1) Familiarity has significant influence on R-E fit; (2) Self-efficacy has significant influence on R-E fit; (3) Restorative environment features have significant influence on R-E fit; (4) Recreational environment hassles have little significant influence on R-E fit; (5) R-E fit has significant influence on flow experience; (6) R-E fit has significant influence on recreational satisfaction; and (7) R-E fit has significant influence on destination loyalty. Accordingly, at the end of this study, suggestions are offered for future research, along with implications for tourism management

Using The Theory Of Planned Behavior To Explain And Predict Behavior Intentions in Taiwan

This study aims to use the theory of planned behavior to verify undergraduates’ behavioral intentions regarding their participation in aquatic sports. Undergraduates in Taiwan serve as the research subjects and a survey method employs questionnaires. A total of 200 valid questionnaires were received out of 230, thus giving a valid response rate of 86.9%. The information was analyzed with AMOS 19.0. The research results show that: First, attitudes do not have a significant influence on the behavioral intentions; Second, subjective norms do not have a significant influence on the behavioral intentions; and Third, perceived behavior control has a significant influence on the behavioral intentions. It is expected that the results of this study can serve as reference for strategies and measures to promote aquatic sports in universities as well as agencies in the aquatic sports industry in Taiwan

Bonding and high-temperature reliability of NiFeMo alloy/n-type PbTe joints for thermoelectric module applications

PbTe is an extremely important thermoelectric (TE) material, due to its high TE conversion efficiency. Consequently, our effort focuses on developing PbTe-based TE modules, which requires developing novel approaches for bonding metallic contacts to PbTe. In this study, Fe, Mo, and NiFeMo alloy foils were directly bonded to n-type PbTe using a rapid hot press at 600, 700, or 800 °C under a pressure of 40 MPa and for various holding times. We find that in the case of Fe and Mo, it is difficult to form a metallurgically bonded high strength joint with PbTe. However, we find that NiFeMo alloy does effectively bond to PbTe at 700 °C, but not at 600 °C. Significant liquid Pb, which might be due to the reaction of PbTe with Ni, is found that penetrates along the NiFeMo grain boundaries near NiFeMo/PbTe joints during bonding at 700 °C where the extent of liquid Pb penetration can be controlled with the time of bonding. Furthermore, the Seebeck coefficient of bulk PbTe with NiFeMo contacts is similar to that without NiFeMo contacts. Finally, the accelerated thermal aging of NiFeMo/PbTe elements at 600 °C for 240 h shows that the failure mechanism of NiFeMo/PbTe joints under operating conditions is the continued formation and penetration of eutectic liquid NiFeMo–PbTe and liquid Pb along the NiFeMo grain boundaries

Distributed Training Large-Scale Deep Architectures

Scale of data and scale of computation infrastructures together enable the current deep learning renaissance. However, training large-scale deep architectures demands both algorithmic improvement and careful system configuration. In this paper, we focus on employing the system approach to speed up large-scale training. Via lessons learned from our routine benchmarking effort, we first identify bottlenecks and overheads that hinter data parallelism. We then devise guidelines that help practitioners to configure an effective system and fine-tune parameters to achieve desired speedup. Specifically, we develop a procedure for setting minibatch size and choosing computation algorithms. We also derive lemmas for determining the quantity of key components such as the number of GPUs and parameter servers. Experiments and examples show that these guidelines help effectively speed up large-scale deep learning training

Dynamic response of a polymer-stabilized blue-phase liquid crystal

Fast response time is the most attractive feature of polymer-stabilized blue phase liquid crystals (PS-BPLCs). We have investigated the dynamic response of a PS-BPLC under various electric fields and found that the response time becomes slower as the applied electric field exceeds a critical field. Further analyses of experimental data reveal that two relaxation processes are involved. Possible mechanism is proposed to explain the behavior of each process. These results provide useful guidelines for achieving fast response time without hysteresis