Chinese calligraphy, a cornerstone of East Asian cultural heritage, is aesthetically governed by "Qi Yun"(气韵), or "spirit resonance," a concept deeply tied to the work’s spatial composition and vitality. Traditional analysis of "Qi Yun" relies on subjective interpretation, hindering objective evaluation and digital preservation. This study bridges this gap by introducing a computational framework to quantify "Qi Yun" through the analysis of multi-scale spatial features. We focus on the holistic structure of calligraphic works by extracting a comprehensive set of metrics that correspond to classical aesthetic principles. These include brightness distribution (reflecting "void and substance"), boundary point distribution (capturing spatial texture), white space structure via Voronoi diagrams and space syntax (analyzing "Bu Bai" or negative space), and columnar analysis (quantifying vertical rhythm). We forgo granular stroke-level analysis to concentrate on the overall composition, or "Zhang Fa"(章法). The efficacy of this approach is substantiated through a comparative case study of different versions of Mi Fu’s "Sui Feng Tie"(岁丰帖), demonstrating the method’s ability to quantify subtle stylistic variations in spatial layout. By creating a dialogue between ancient aesthetics and modern data science, this research offers a new, objective tool for calligraphic analysis and contributes to the digital preservation of this intangible cultural heritage.

Text-to-image (T2I) generation has advanced significantly in recent years, yet current models often struggle with prompts that imply causal sequences or require knowledge of culturally grounded entities. This limitation stems from a fundamental "semantic gap" between a user’s rich intent and the model’s statistical interpretation of text. To address these limitations, we propose a causality-aware multimodal framework that integrates large language models (LLMs), visual-language verification, and domain-specific image retrieval within an iterative, self-correcting pipeline. The system first decomposes prompts into structured representations of causal chains and named entities. It then retrieves aligned visual references from a multimodal knowledge base to ground these abstract concepts. These components are fused into an enriched multimodal prompt for a frozen-backbone diffusion model. A verification module, powered by a Vision-Language Model (VLM), evaluates the causal and semantic consistency of the generated output, triggering a refinement loop when necessary. This closed-loop design enables more coherent, grounded, and context-sensitive image synthesis, particularly in complex or culturally nuanced scenarios. Our approach expands the expressive capacity of T2I systems by explicitly modeling and integrating the unspoken details of physical logic and domain knowledge, thereby bridging the semantic gap and producing images that are more faithful to user intent.

Supporting student cognitive well-being in today’s learning environments is a key priority. This study employed functional near-infrared spectroscopy to measure prefrontal cortex activation in 15 healthy adults during a color-word Stroop task with four emotional-color combinations. Results demonstrated significant left prefrontal activation for negative emotional words, alongside enhanced neural engagement in response to positive color stimuli, while incongruent pairings induced measurable cognitive conflict. Oxygenated hemoglobin responses at 850 nm provided optimal sensitivity for detecting these neural patterns. These findings suggest design principles for student-centered cognitive training interfaces, strategically integrating emotional semantics and color cues to maximize prefrontal engagement, and offering pathways to enhance educational interactions through neuroscience-informed visual interface design.

Maintaining machinery wells, key to groundwater sustainability, is vital for managing these precious resources. In keeping with the need for effective groundwater management, this study introduces a screening process leveraging the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm and incorporating point of interest (POI) and spatial correlation assessment. Specifically, this approach was utilized in the Fangshan District, Beijing, to detect illegal water extraction wells and bolster water resource management. By integrating POI and land use categorization, we performed a water demand overlay analysis, considering public water provision and well distribution. ArcGIS spatial analysis helped us pinpoint POIs with significant water demand. The DBSCAN clustering algorithm was then employed to scrutinize potential problematic wells. The credibility and practicality of this methodology were confirmed through field studies, meeting current governance standards. The study identified 14 potential unregistered wells, of which 5 were confirmed to be unauthorized, primarily located in Shidu and Zhangfang Towns. Field checks confirmed these findings, highlighting the need for improved groundwater management and validating our method’s reliability. Based on these findings, we propose additional steps to enhance groundwater extraction management. This research shows how the use of POI data and the DBSCAN algorithm can aid groundwater resource management in Fangshan District and potentially serve as a model for other regions.

The key to preserving traditional crafts lies in living transmission, which is inseparable from sustaining artistic production, audience consumption, and progressive innovation with the physical media. As HCI researchers, we focus on the hybrid crafts field, which involves numerous cross-disciplinary integration cases between traditional craftsmanship and digital technology at the physical level, providing inspiration for innovating and enlivening traditional crafts. We conducted a multi-perspective review of 85 hybrid craft articles related to traditional crafts over the past decade, considering aspects such as craft categories, digital technology, target users, and research areas. Through reflection, we propose a design framework for fostering innovation and revitalizing traditional crafts. This paper aims to offer insight into the innovation and enlivenment of traditional crafts through a hybrid craft perspective while also serving as a first review of the hybrid craft field from the traditional craftsmanship perspective.

While there has been rapid growth in smart home research from a technical perspective– focusing on home automation, devices, software, and protocols– few review papers examine the human-centered perspective. A human-centered focus is crucial for achieving the goals of providing natural, convenient, comfortable, friendly, and safe user experiences in the smart home. To understand key innovations in human-centered smart home research, we analyzed keyword changes over time via 19,091 papers from 2000 to 2022, then selected 55 papers from high-impact venues in the last five years, and summarized them through a combination of qualitative and quantitative methods. Our analysis revealed five research trends with unique characteristics and interdependence. Drawing on this review, we elaborate on the future of smart home design research with respect to multidisciplinary development, stakeholder involvement, and the shift of design implications.