僑光科技大學

Drawing on data from the Asian Barometer Survey (ABS) and employing latent path analysis, this study explores the complex relationship between political contestation and democratic identity formation in Hong Kong and Taiwan. It measures geopolitical antagonism through negative public perceptions of China's influence and examines how these perceptions affect support for democracy via political action, trust in institutions, and changing social values. The findings reveal divergent outcomes: in Taiwan, external threats have strengthened civic identity and democratic resilience, while in Hong Kong, the internalization of authority loss under Chinese control has eroded institutional trust and autonomy. Despite these differences, rejection of authoritarianism remains high in both contexts with Chinese antagonism playing a pivotal role in democratic identity formation. By highlighting this dynamic, the article advances broader debates on democratization in contested polities and the conditions under which they can sustain legitimacy and effective institutional functioning.

Dissociative symptoms are prevalent and disabling, but little is known about what factors can longitudinally predict dissociative symptoms. This study examined the protective role of perceived social support against dissociative symptoms. We analyzed data from the International Dissociative Depression Survey Project (N = 152) and the International Female Mental Health Survey Project (N = 293). In both samples, participants completed validated measures of dissociative symptoms and perceived social support at baseline, and then reported their dissociative symptoms after approximately 6 to 12 months. We tested the hypothesis that perceived social support would predict subsequent dissociative symptoms in each sample and see whether the results could be replicated across different samples. After controlling for demographic variables, childhood trauma, and baseline dissociative symptoms, baseline perceived social support was significantly associated with fewer dissociative symptoms at follow-up (β = − 0.129 to − 0.198, p = .001). The results were replicated across the two samples. This study contributes to the very limited literature on the longitudinal predictors of dissociative symptoms. Our results point to the critical role of social-interpersonal and family interventions in preventing and treating dissociative symptoms.

Purpose This study integrates the knowledge-based view (KBV) and dynamic capability theory (DCT) to explore how market knowledge enhances resilience and robustness in tourism supply chains (TSCs), shaping pre- and post-disruption performance. It examines market intelligence as a critical resource for risk identification, strategic adaptation and operational stability. Resilience enables firms to anticipate and adapt to disruptions, while robustness ensures operational continuity amid external shocks. Design/methodology/approach A stratified random survey of 415 travel agencies in Taiwan was conducted. Partial least squares-structural equation modeling (PLS-SEM) was employed to analyze the effects of resilience and robustness mediation. PLS-SEM was chosen for its ability to handle complex relationships and latent constructs, ensuring robust empirical validation. Findings Market knowledge enhances resilience and robustness, which mediate its impact on TSC performance. Resilience fosters proactive crisis response, while robustness ensures stability through supplier diversification and digital integration. These insights extend beyond Taiwan, offering global relevance for tourism markets facing pandemics, geopolitical instability and climate disruptions. Practical implications To enhance adaptability, travel agencies should adopt AI-driven market analytics, supplier diversification and scenario-based crisis management. Policymakers can develop regulatory frameworks for crisis preparedness and sustainable supply chains, ensuring economic stability and long-term resilience. This research supports sustainable tourism, promoting adaptive, data-driven and resilient business models in a volatile environment. Originality/value This study applies KBV and DCT to tourism supply chain resilience, emphasizing market knowledge as a key to proactive (resilience) and structural (robustness) crisis responses. Unlike prior studies viewing resilience as reactive, this research highlights its anticipatory role, while robustness emerges as a knowledge-driven enabler of stability. By distinguishing pre- and post-disruption performance, this study advances crisis management insights with global relevance.

Job cycle time bounding is crucial for factories, as the upper and lower bounds of job cycle time can be used for various production control and management activities. However, precise job cycle time bounding remains a challenging task. Deep learning (DL) applications hold promise for overcoming this challenge. Convolutional neural networks (CNNs), as a highly attractive deep learning (DL) architecture, have achieved remarkable results in image and speech recognition, but have not yet been applied to job cycle time prediction or bounding. To explore the potential of CNNs in this field, this study proposes a fuzzy convolutional neural network (FCNN) approach to further enhance the effectiveness of job cycle time bounding. In the FCNN approach, a CNN with dropout is first constructed for job cycle time prediction. Job cycle time related data are then resized to be compatible for the CNN. Subsequently, network parameters are fuzzified to derive the lower and upper bounds of job cycle time. The FCNN approach has been experimentally applied to a wafer fabrication case. According to the experimental results, the CNN outperformed a deep neural network (DNN) in job cycle time prediction by reducing the mean absolute percentage error (MAPE) by up to 33