Search

Covalent organic frameworks (COFs) are of great interest in the energy and optoelectronic fields due to their high porosity, superior thermal stability, and highly ordered conjugated architecture, which are beneficial for charge migration, charge separation, and light harvesting. In this study, polyimide COFs (PI-COFs) are synthesized through the condensation reaction of pyromellitic dianhydride (PMDA) with tris(4-aminophenyl) amine (TAPA) and then doped in the TiO

PurposeThis study aims to develop a hierarchical topic analysis tool (HTAT) based on hierarchical Latent Dirichelet allocation (hLDA) to support digital humanities research that is associated with the need of topic exploration on the Digital Humanities Platform for Mr. Lo Chia-Lun’s Writings (DHP-LCLW). HTAT can assist humanities scholars on distant reading with analysis of hierarchical text topics, through classifying time-stamped texts into multiple historical eras, conducting hierarchical topic modeling (HTM) according to the texts from different eras and presenting through visualization. The comparative network diagram is another function provided to assist humanities scholars in comparing the difference in the topics they wish to explore and to track how the concept of a topic changes over time from a particular perspective. In addition, HTAT can also provide humanities scholars with the feature to view source texts, thus having high potential to be applied in promoting the effectiveness of topic exploration due to simultaneously integrating both the topic exploration functions of distant reading and close reading.Design/methodology/approachThis study adopts a counterbalanced experimental design to examine whether there is significant differences in the effectiveness of topic inquiry, the number of relevant topics inquired and the time spent on them when research participants were alternately conducting text exploration using DHP-LCLW with HTAT or DHP-LCLW with Single-layer Topic Analysis Tool (SLTAT). A technology acceptance questionnaire and semi-structured interviews were also conducted to understand the research participants' perception and feelings toward using the two different tools to assist topic inquiry.FindingsThe experimental results show that DHP-LCLW with HTAT could better assist the research participants, in comparison with DHP-LCLW with SLTAT, to grasp the topic context of the texts from two particular perspectives assigned by this study within a short period. In addition, the results of the interviews revealed that DHP-LCLW with HTAT, in comparison with SLTAT, was able to provide a topic terms that better met research participnats' expectations and needs, and effectively guided them to the corresponding texts for close reading. In the analysis of technology acceptance and interview data, it can be found that the research participants have a high and positive tendency toward using DHP-LCLW with HTAT to assist topic inquiry.Research limitations/implicationsThe Jieba Chinese word segmentation system was used in the Mr. Lo Chia-Lun’s Writings Database in this study, to perform word segmentation on Mr. Lo Chia-Lun’s writing texts for topic modeling based on hLDA. Since Jieba word segmentation system is a lexicon based word segmentation system, it cannot identify new words that have still not been collected in the lexicon well. In this case, the correctness of word segmentation on the target texts will affect the results of hLDA topic modeling, and the effectiveness of HTAT in assisting humanities scholars for topic inquiry.Practical implicationsAn HTAT was developed to support digital humanities research in this study. With HTAT, DHP-LCLW provides hmanities scholars with topic clues from different hierarchical perspectives for textual exploration, and with temporal and comparative network diagrams to assist humanities scholars in tracking the evolution of the topics of specific perspectives over time, to gain a more comprehensive understanding of the overall context of the texts.Originality/valueIn recent years, topic analysis technology that can automatically extract key topic information from a large amount of texts has been developed rapidly, but the topics generated from traditional topic analysis models like LDA (Latent Dirichelet allocation) make it difficult for users to understand the differences in the topics of texts with different hierarchical levels. Thus, this study proposes HTAT which uses hLDA to build a hierarchical topic tree with a tree-like structure without the need to define the number of topics in advance, enabling humanities scholars to quickly grasp the concept of textual topics and use different hierarchical perspectives for further textual exploration. At the same time, it also provides a combination function of temporal division and comparative network diagram to assist humanities scholars in exploring topics and their changes in different eras, which helps them discover more useful research clues or findings.

Item concept modeling is commonly achieved by leveraging textual information. However, many existing models do not leverage the inferential property of concepts to capture word meanings, which therefore ignores the relatedness between correlated concepts, a phenomenon which we term conceptual “correlation sparsity.” In this paper, we distinguish between word modeling and concept modeling and propose an item concept modeling framework centering around the item concept network (ICN). ICN models and further enriches item concepts by leveraging the inferential property of concepts and thus addresses the correlation sparsity issue. Specifically, there are two stages in the proposed framework: ICN construction and embedding learning. In the first stage, we propose a generalized network construction method to build ICN, a structured network which infers expanded concepts for items via matrix operations. The second stage leverages neighborhood proximity to learn item and concept embeddings. With the proposed ICN, the resulting embedding facilitates both homogeneous and heterogeneous tasks, such as item-to-item and concept-to-item retrieval, and delivers related results which are more diverse than traditional keyword-matching-based approaches. As our experiments on two real-world datasets show, the framework encodes useful conceptual information and thus outperforms traditional methods in various item classification and retrieval tasks.

The jaw coupling with a flexible spacer is frequently used in the torque transmission between shafts with misalignment for machinery. Its torsional stiffness and limit torque closely determine the operational capacity and the dynamic characteristics of the system because the coupling is usually the most flexible link in the driving chain. In this study, the optimal design of a jaw coupling with an elastomeric spacer was investigated using the Taguchi method by considering four design factors: the tightening method of the clamping bolts, the tightening torque of the clamping bolts, the hardness of the thermoplastic polyether ester elastomer spacer and the installation angle between the two end blocks of the coupling. All specimens were tested by using an in-house torsion tester to record the torque-angular deformation responses. The results showed good reliability and repeatability, with a coefficient of variance within 5%. Spacer's hardness was found to be the most significant factor regarding the torsional stiffness, while the magnitude of the clamping torque had the most critical role in the limit torque. The estimation formulae for the torsional stiffness and limit torque of the jaw coupling were obtained by using the statistical regression of the measured data, respectively. Both formulae predicted the performance of the optimal designs within 5% of error compared to the confirmation tests.

This study focuses on the cooperative attitude and intention of retail stores in Taiwan to cooperate with the government’s related pandemic prevention measures. The study is based on the Theory of Planned Behavior (TPB). The study includes factors such as perceived risk of infection, job stress, pandemic prevention IT (information technology) convenience, pandemic prevention attitude, and pandemic prevention intention. Pandemic prevention attitude is used as a mediating variable to establish the research framework. This study collected research data through a questionnaire survey. A total of 457 valid questionnaires were collected through an electronic questionnaire platform. The findings showed that perceived risk of infection and pandemic prevention IT convenience had a positive and significant effect on pandemic prevention attitude (β = 0.567; β = 0.422) and pandemic prevention intention (β = 0.424; β = 0.296). Job stress has a significant negative effect on attitude (β = −0.173). In addition, job stress influenced intention through attitudes. Finally, perceived risk, job stress, and IT convenience had high explanatory power (R2 = 0.706) on attitudes. Perceived risk, IT convenience, and attitude also had moderate explanatory power (R2 = 0.588) on prevention intention. The study also suggests practical recommendations to improve and cooperate with pandemic prevention intention.