Congenital aphantasia refers to a lifelong absence of voluntary visual imagery. Imagery varies substantially across individuals and may provide insight into broader patterns of cognitive, emotional, and interoceptive functioning. However, imagery differences remain underexamined within the individual-differences literature. The present study investigated whether variation in imagery vividness is associated with bodily awareness, neurodevelopmental traits, and early-life experience.Imagery vividness was assessed using the Plymouth Sensory Imagery Questionnaire (PSI-Q) in an online sample comprising individuals with core aphantasia (PSI-Q = 0; n = 330), hypofantasia (0 < PSI-Q ≤ 4; n = 755), typical imagery (4 < PSI-Q < 9; n = 216), and hyperphantasia (9 ≤ PSI-Q ≤ 10; n = 123). Participants completed self-report measures of autonomic symptoms, cardiac symptomatology, childhood experiences, ADHD traits, autistic traits, anxiety, postural symptoms, and hypermobility.Both aphantasia groups reported reduced cardiac symptoms relative to typical imagers, while core aphantasia additionally showed reduced POTS symptoms. Both aphantasia groups exhibited elevated ADHD and autistic trait. Emotional neglect was higher in both aphantasia groups relative to hyperphantasia, while hyperphantasia showed elevated hypermobility traits.These findings suggest that imagery vividness reflects individual differences linked to bodily awareness, neurodevelopmental traits, and early experience.
Retrieval-Augmented Generation enhances large language models by incorporating external knowledge, but deploying it in sensitive scenarios risks privacy leakage via malicious prompts. To address this, we propose a multi-agent framework that sanitizes retrieved content through semantic rewriting. By employing three specialized agents for privacy extraction, semantic analysis, and reconstruction, our approach collaboratively removes sensitive identifiers while preserving the semantic core. We evaluate the framework on the ChatDoctor and Wiki-PII datasets across six large language models. Experimental results demonstrate a significant reduction in privacy leakage under targeted attacks. For instance, we reduced targeted information exposure in LLaMA-3-8B from 144 instances in the baseline to just 1. Furthermore, we maintain strong contextual fidelity with a BLEU-1 score of 0.122, outperforming the existing SAGE method's 0.117. Finally, the framework operates as an asynchronous preprocessing module, introducing no additional latency to online inference, as all rewriting is executed as a one-time offline preprocessing step. To promote reproducibility, the source code of this work is publicly available at https://github.com/foursoils/Privacy-Preserving-RAG.
In modern society, an increasing number of people are suffering from the harmful effects of chronic stress, while data-enabled reflection has been shown to be an effective approach to stress management. However, most existing human-data interaction designs primarily focus on retrospective data review, often failing to foster engaging and meaningful reflection. In this paper, we present CloudTrip, an immersive application that represents the users' data throughout a day as an experiential and interactive digital story for mental health selfreflection. Specifically, CloudTrip metaphorically conveys users' daily stress-related physiological data into narrative experiences by dynamically mapping the data onto changes in visual and auditory elements of characters and virtual environments. Users can record reflective content during the experience and receive responsive feedback generated by a large language model, thereby forming an interactive narrative loop that further deepens reflective engagement. We conducted a 14-day within-subject study with 20 participants, comparing CloudTrip with a traditional line-chart visualization using a mixed-methods evaluation to assess its impact on the reflection experience and reflection support. Results show that CloudTrip was associated with a cardiac autonomic pattern compatible with higher arousal, increases in self-reported valence, arousal, and dominance, more favorable motivational ratings for the experience, stronger perceived support for reflection, and promise for repeated use. Finally, we discuss design implications for integrating data-enabled multisensory storytelling techniques into human-data interaction systems to promote everyday reflective behavior for mental health management.
Bibliometric analysis is a powerful means of evaluating research. This article employs a sequential mixed-methods approach to enrich the understanding of bibliometric analysis and presents a structured guide to maximize the technique’s benefits. A thematically synthesized narrative commentary on bibliometric review papers, based on input from 53 senior editorial members of leading hospitality and tourism journals, constitutes the study’s foundation. Cutting-edge literature comprising 195 bibliometric investigations is also considered. Our evidence-based guidance provides scholars with a roadmap for conducting state-of-the-art bibliometric analyses. These suggestions are intended to improve the method’s quality and accessibility, affirming its utility. Ultimately, this paper seeks to enhance bibliometric studies’ clarity, rigor, and efficacy by presenting forward-looking, real-world insights. Its step-by-step guide will facilitate decision-making and advance the field’s theoretical and managerial understanding.
Renewable energy transitions change rather than remove the geopolitical dimensions of energy systems. In recent years, geopolitical fragmentation, strategic rivalry, and geoeconomic insecurity have increased the importance of security, resilience, and strategic dependence within Europe's energy transition. Against this background, this study examines how geopolitical risk exposure is associated with four observable transition adjustment outcomes across 25 EU member states from 2001 to 2023: external energy dependence, domestic renewable energy capacity, economic globalization, and climate-significant public investment. Geopolitical risk exposure was measured by interacting the annual Global Geopolitical Risk Index with countries' geographical proximity to Russia, allowing the empirical models to incorporate both country and year fixed effects. The empirical analysis combined conventional two-way fixed-effects estimation with double machine learning (DML), alternative exposure specifications, robustness analyses, moderation analyses, and heterogeneity analyses. The results showed that greater geopolitical risk exposure is associated with lower external energy dependence, greater domestic renewable energy capacity, and lower economic globalization, while its relationship with climate-significant public investment is statistically insignificant. The moderating and heterogeneity analyses further indicated that these relationships vary with countries' energy-consumption levels, innovation capability, industrial structure, Russian energy dependence, and institutional capacity. Overall, the findings indicate selective rather than uniform adjustment across Europe's energy transition.
