Proton batteries have brought new research directions in the field of fuel cells and energy storage. Although our R&D team has developed a prototype of the proton battery stack, there are still some problems to be solved, such as leakage and unstable power generation. In addition, simultaneously and accurately measuring multiple key physical parameters inside a proton battery stack remains challenging. The present measurement methods are external or single parameters, that has become a limiting factor. These parameters (voltage, current, temperature, flow, humidity, pressure, oxygen and hydrogen) significantly affect the performance, life and safety of the proton battery stack. To address these challenges, this study developed a micro hydrogen sensor using the Micro-electro-mechanical systems (MEMS) technology, which was integrated with the previously developed seven-in-one microsensor, the mask and layout were re-designed to increase the number of microsensors, which were combined with a Flexible Printed Circuit (FPC). Finally, the development of a flexible eight-in-one microsensor was completed, so as to improve the output and overall operating efficiency of the microsensor, and provide a reliable internal multi-parameter real-time microscopic monitoring and diagnostic tool for the development of proton battery stacks.
Individuals’ motivation explains their efforts to meet their preference in consumption situations within family. This study aims to examine the relative importance of intrinsic and external elements of adolescents’ tourist motivation in explaining their influence over parents’ decisions regarding family vacation. We also look at the moderating role of disagreement resolution approach used in their family life. Data from 571 responses by adolescents in Taiwan revealed that seeking force (SF) is positively related to their perceived influence across the stages of family travel decisions. In contrast, adolescents’ escape force (EF) is unrelated to these outcomes. Furthermore, adolescents’ problem-solving orientation (PSO) to conflict resolution appears to strengthen the relatedness of SF to adolescents’ perceived influence at the initiation stage (II), the search and evaluation stage (SEI) and the final decision stage (FDI). The non-problem-solving orientation (NPSO) to conflict resolution appears to weaken the relationship between EF and SEI. We also found an inverse-V-shaped pattern of influence as speculated. This contrasts with the conventional view of this as a declining or V-shaped pattern in family purchase decisions.
Human resources are concerned with recruiting, training and keeping talent. The correct selection of personnel is key to the success of a business because regardless of the outstanding business strategy, those who put it into practice are most important. A carefully developed strategy is as bad as having no strategy if the wrong person is placed in the wrong position. There are no studies to predict the competency of job applicants based on dimensional values such as level of knowledge, self-motivation, self-concern and role perception. This study proposes a prediction model that uses AI (Artificial Intelligence), this includes a quantitative mathematical model that identifies the level of personnel competency to optimize job performance. The model verifies the discrete and continuous relationship between competency dimensions and their synergetic effects. Four AI algorithms are used to train a data set that contains 362 data elements, consisting of two optimized combinations of dimensional values in step 1, and 360 data elements that are derived from the dimensional relationship that produces the synergetic effects. The proposed model predicts the competence of job applicants and compares this competence with the value gives optimized job performance to optimize the selection of human resources.
Predicting the wafer material removal rate (MRR) is an important step in semiconductor manufacturing for total quality control. This work proposes a deep learning model called a fusion network to predict the MRR, in which we consider separating features into shallow and deep features and use the characteristics of deep learning to perform a fusion of these two kinds of features. In the proposed model, the deep features go through a sequence of nonlinear transformations and the goal is to learn the complex interactions among the features to obtain the deep feature embeddings. Additionally, the proposed method is flexible and can incorporate domain knowledge into the model by encoding the knowledge as shallow features. Once the learning of deep features is completed, the proposed model uses the shallow features and the learned deep feature embeddings to obtain new features for the subsequent layers. This work performs experiments on a dataset from the 2016 Prognostics and Health Management Data Challenge. The experimental results show that the proposed model outperforms the competition winner and three ensemble learning methods. The proposed method is a single model, whereas the comparison methods are ensemble models. Besides the experimental results, we conduct extensive experiments to analyze the proposed method.
This study used a measurement model to investigate adolescents in 25 countries, in an effort to understand their motivations for attending family travel. In general, both cultural region and culture type were related to travel motivation patterns and studentized residual patterns. In addition, overall, a country's level of development and its cultural dimensions were more strongly correlated with females' travel motivations of interpersonal escape and seeking than with those motivations in males. We also confirmed a U-shaped relationship of cultural response bias in certain measures of motivation components in connection with three indicators: masculinity, uncertainty avoidance, and family score.
