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This study aims to integrate and revalidate previously proposed various structural models in understanding residents’ attitudes and behaviors in relation to mega-events before the events.

This study focussed on the 2022 Beijing Winter Olympics and used a questionnaire-based quantitative survey prior these events. A PLS-SEM analysis was run on a sample of 473 residents, in testing relationships between residents’ trust, perceived impacts, support for hosting and subjective well-being.

Results revalidate propositions from previous research, but suggest key contextual differences in light of biosecurity risks. Residents’ perceived positive (cultural) and negative (environmental) impacts affect their support for mega-events, and their perceived positive (economic and cultural) and negative (social) impacts affect their subjective well-being. Variances in the relationships were found for those who perceive a high biosecurity risk.

The data were collected from one mega-event, and thus the findings of this study are highly contextualized.

This research suggest that mega-event organizers should put effort into promoting the benefits of hosting mega-events and work collaboratively with stakeholders to reduce potential negative costs and risks as well as increase resident well-being via bringing in economic and cultural benefits.

This research focusses on social well-being during and post COVID in relation to the hosting of a mega-event.

The data were collected from the 2022 Beijing Winter Olympics, a mega-event that, because of COVID-19 and restricted spectator flows, potentially had characteristics quite different from that of other Winter Olympics or sporting mega-events.

Entity relation extraction is an important research direction to obtain structured information. However, most of the current methods are to determine the relations between entities in a given sentence based on a stepwise method, seldom considering entities and relations into a unified framework. The joint learning method is an optimal solution that combines relations and entities. This paper aims to optimize hierarchical reinforcement learning framework and provide an efficient model to extract entity relation.

This paper is based on the hierarchical reinforcement learning framework of joint learning and combines the model with BERT, the best language representation model, to optimize the word embedding and encoding process. Besides, this paper adjusts some punctuation marks to make the data set more standardized, and introduces positional information to improve the performance of the model.

Experiments show that the model proposed in this paper outperforms the baseline model with a 13% improvement, and achieve 0.742 in F1 score in NYT10 data set. This model can effectively extract entities and relations in large-scale unstructured text and can be applied to the fields of multi-domain information retrieval, intelligent understanding and intelligent interaction.

The research provides an efficient solution for researchers in a different domain to make use of artificial intelligence (AI) technologies to process their unstructured text more accurately.

This paper aims to present a new kinematics mapping method based on dynamic equivalent points. In teleoperation, this method enables a robotic (follower) arm to mimic human (leader) arm postures and avoid obstacles in a human-like manner.

The information of the human arm is extracted based on the characteristics of human arm motion, and the concept of equivalent points is introduced. Then, an equivalent point is determined to transform the robotic arm with a nonhuman-like kinematic structure into an anthropomorphic robotic arm. Based on this equivalent point, a mapping method is developed to ensure that the two arms are similar. Finally, the similarity between the human elbow angle and robot elbow angle is further improved by using this method and an augmented Jacobian matrix with a compensation coefficient.

Numerical simulations and physical prototype experiments are conducted to verify the effectiveness and feasibility of the proposed method. In environments with obstacles, this method can adjust the position of the equivalent point in real time to avoid obstacles. In environments without obstacles, the similarity between the human elbow angle and robot elbow angle is further improved at the expense of the end-effector accuracy.

This study presents a new kinematics mapping method, which can realize the complete mapping between the human arm and heterogeneous robotic arm in teleoperation. This method is versatile and can be applied to various mechanical arms with different structures.

Drawing upon the conservation of resources theory, this study aims to investigate whether, how and when salespeople’s substantive attribution of the organization’s corporate social responsibility (CSR) affects value-based selling (VBS). The authors argue that salespeople’s substantive CSR attribution increase value-based selling through two mechanisms (i.e. by lowering emotional exhaustion and increasing empathy), and treatment by customers can increase or decrease the strength of these relationships.

B2B salespeople working in various industries in China were recruited through snowball sampling to participate in the study. There were 462 volunteers (57.58% women; aged 30–55; tenure ranging from six months to 15 years) who provided valid self-report questionnaires.

Hierarchical multiple regression supported the association between salespeople’s substantive CSR attribution and VBS. The results showed that salespeople’s emotional state (i.e. emotional exhaustion and empathy) mediated the association between substantive CSR attribution and VBS. As expected, salespeople’s experiences of customer incivility weakened the mediating effect of emotional exhaustion; contrary to expectations, customer-initiated interpersonal justice weakened the mediation effect of empathy.

This study makes a unique contribution to the existing marketing literature by first investigating the role of salespeople’s attribution of CSR motives in facilitating their VBS, which answers the call to identify factors that predict VBS. In addition, to the best of the authors’ knowledge, the authors are the first to test salespeople’s emotions as a mechanism of the link between their CSR attributions and selling behaviors.

The preservation and disappearance of indigenous people’s traditional knowledge system, under mainstream social culture immersion and fusion, have presented a dynamic and changing acculturation interactive relationship impacting Truku women’s health concepts. Thus, the purpose of this paper is to explore how the traditional Gaya knowledge system and mainstream culture confinement care model affect the beliefs and behaviours of postpartum self-care amongst contemporary Truku women.

An ethnographic semi-structured method, based on cultural care factors and the Leininger Sunrise Model, was conducted to interview 17 Truku women with childbearning experience in eastern Taiwan. As data were collected, UDIST Vivo 11.0 software was applied for analysis.

Amongst the three knowledge system categories, namely, traditional, mainstream and reconstruction, the traditional knowledge system, including Gaya norms, provides the overall cultural value of a Truku family. While taboo is inherited through the experience of the elders, the mainstream knowledge system favours the Han. However, the reconstruction knowledge system highlights the “functional” response strategies based on Truku women’s comfort and conveniences.

Limited relevant studies have focused on the health and postpartum self-care knowledge of ethnic Truku women in Taiwan. The results are expected to provide clinical medical personnel with a reference and strengthen cultural sensitivity and the ability to implement the cultural congruency care of postpartum indigenous women in Taiwan.

Community question answering (CQA) websites provide an open and free way to share knowledge about general topics on the internet. However, inquirers may not obtain useful answers and those who are qualified to provide answers may also miss opportunities to share their expertise without any notice. To address this problem, the purpose of this paper is to provide the means for inquirers to access archived answers and to identify effective subject matter experts for target questions.

This paper presents a question answering promoter, called QAP, for the CQA services. The proposed QAP facilitates the use of filtered archived answers regarded as explicit knowledge and recommended experts regarded as sources of implicit knowledge for the given target questions.

The experimental results indicate that QAP can leverage knowledge sharing by refining archived answers upon creditability and distributing raised questions to qualified potential experts.

This proposed method is designed for the traditional Chinese corpus.

This paper proposed an integrated framework of answer selection and expert finding uses the bottom-up multipath evaluation algorithm, an underlying voting model, the agglomerative hierarchical clustering technique and feature approaches of answer trustworthiness measuring, identification of satisfied learners and credibility of repliers. The experiments using the corpus crawled from Yahoo! Knowledge Plus under designed scenarios are conducted and results are shown in fine details.