Reading assistants provide users with additional information through pop-ups or other interactive events which might interrupt the flow of reading. We propose that unnoticeable changes can be made in a given text during blinks while the vision is obscured for a short period of time. Reading assistants could make use of such change blindness to adapt text in real time and without infringing on the reading experience. We developed a system to study blink-induced change blindness. In two preliminary experiments, we asked five participants to read six short texts each. Once per text and during a blink, our system changed a predetermined part of each text. In each trial, the intensity and distance of the change were systematically varied. Our results show that text changes — although obvious to bystanders — were difficult to detect for participants. Concretely, while changes that affected the appearance of large text parts were detected in 80% of the occurrences, no line-contained changes were detected.

Elevated postprandial glucose levels present a global epidemic and a major challenge in type-2 diabetes (T2D) management. A key barrier to developing effective dietary interventions for T2D management is the wide inter-individual variation in glycemic and behavioral responses, which limits the impact of one-size-fits-all recommendations. To enable personalized dietary prompts for glycemic control, it is critical to first predict an individual’s susceptibility to elevated postprandial (PPG) levels—or state of momentary vulnerability to PPG excursions. We examined the feasibility of personalized models to predict PPG excursions,and their associated vulnerability states, in the daily lives of 67 Chinese adults with T2D (Mage = 61.39; median = 63.00; 35 women; 2,463 glucose observations). We developed machine learning models trained on past individual observations to predict the next-in-time PPG excursion, using continuous glucose monitoring (CGM) data or CGM data combined with manually-logged meals and glucose-lowering agent intake. On average, personalized models predicted PPG excursions (F1-score: M = 75.88%; median = 78.26%), with substantial variation in predictability across individuals. Notably, no two individuals shared the same dietary and temporal predictors of PPG excursions. This study is the first to predict individual vulnerability states to glucose responses among adults with T2D in China. Findings can help personalize just-in-time adaptive interventions by tailoring dietary prompts based on individuals’ unique vulnerability states to PPG excursions. This approach can inform the development of digital dietary interventions in mHealth apps and clinical decision support tools, thereby helping optimize glycemic control and patient-centered T2D lifestyle management..

Typical everyday physical interactors, such as switches, perform a specific static action upon actuation by a user. For such simple components, this action is independent of the immediate user situation; consideration of this situation typically involves the augmentation of the interactor with specific added interface features (e.g., long-press of a button for dimming). We introduce the "spontaneous context" interaction pattern for everyday interactors where the concrete action is spontaneously adapted based on information about the user situation that the interactors gather and interpret ad hoc. In our approach, the interactor and user hence share no prior relationship and no user data is stored, yet the interactor adapts the action at interaction time. To demonstrate the spontaneous context pattern, we implemented a "plot door": this is an automatic door that differs from classical infrared motion sensor-activated doors by opening only when it is likely that an individual wants to enter. Our plot door uses an infrared sensor that is augmented with our proposed interaction pattern and thereby spontaneously gathers and interprets accelerometer and gyroscope data from the individual to determine whether it should open or not.

Artificial intelligence (AI) is increasingly recognized as a crucial issue in political discourse, yet comparative research on how political perspectives on AI vary across countries, particularly following ChatGPT's public debut, remains limited. This paper presents a cross-national analysis of AI framing in parliamentary debates, exploring their evolution from 2014 to 2024 in the US Congress, EU Parliament, Parliament of Singapore, and Swiss Federal Assembly. Grounded in framing theory and insights from comparative political economy, we assemble a novel data set of parliamentary speech transcripts and employ a mixed-methods approach, combining natural language processing with qualitative content analysis, to identify framing patterns. Our findings reveal a steep surge in AI discussions across all legislatures after ChatGPT's 2022 release, propelled predominantly by ethics and regulation concerns. We also identify distinct national priorities: the US emphasizes defense, Singapore links AI to economic innovation and workforce development, the EU focuses on ethical governance, and Switzerland shows limited but regulation-centric engagement. These divergences highlight how distinct national and geopolitical priorities shape local AI policy debates. We conclude with a discussion of implications for framing research amid technological disruption.

One major goal of eGovernment is the provision of an efficient and cost-effective infrastructure enabling access to online resources for all citizens. While at the beginning this goal was basically related to Internet, today there is growing demand for providing access anywhere and anytime also where wired networks are not available. Major drivers of this development are emerging technologies as WLAN and the growing mobility of inhabitants as well as increasing usage and penetration of mobile phones. Another driver are emerging opportunities for new services for citizens based on mobile technology summarized under the term mGovernment. In order to enable mobile applications on a larger scale an efficient infrastructure is necessary. In this paper the approach of Turku, Southwest Finland, in creating a wireless infrastructure will be described based on a case study.