Pennsylvania State University

Today, people increasingly absorb information about places from online images of locations they have never been to. In the context of contested territories, online images serve as a medium for imagining territories. However, online images of contested territories, just like any other type of mediated information, are channeled through political narratives and ideologies. To quantitatively characterize this digitally mediated territorial imagination, this article proposes a new deep learning approach using Google Cloud Vision API to quantitatively characterize scraped online images of contested territories queried through different toponyms. Through a case study of three disputed territories employing six toponyms, we find that the results of online image queries are different depending on toponymic inputs, and we show how this can lead to different territorial imaginations localized to political and historical contexts. This work contributes these findings using a novel approach that integrates quantitative analytics and critical perspectives on territorial imagination, highlighting future opportunities for deep learning methods to provide insights into real-world geopolitical issues. • Google Cloud Vision API's deep learning approach can analyze characteristics of online images of contested territories. • Different types of online images are retrieved with different toponyms of contested territories. • Variations in the retrieved images reflect various contextualized political narratives surrounding the contested territories.

The demand for portable real-time monitoring technologies for air quality and hazardous gas detection has significantly increased due to the growing interest in healthy living. In particular, nitrogen dioxide (NO 2 ) is a major contributor to air pollution in urban areas, and its concentration can fluctuate rapidly in a short period of time due to traffic emissions and industrial activities. Therefore, it is necessary to develop an ultra-sensitive sensor with fast response and recovery at room temperature. In this study, we demonstrate an NO 2 gas sensor operated at room temperature using Ag-decorated Sr 2 Nb 3 O 10 (ASNO) nanosheets, fabricated through a simple solution-based exfoliation process. This sensor exhibits excellent structural stability and an enhanced response of 935 % to NO 2 gas due to catalytic effect of the Ag decorated surface. It demonstrates fast response and recovery characteristics, which makes it a good candidate for reliable real-time monitoring and repeated use. Room-temperature NO 2 sensing using Ag-decorated 2D nanosheets supports sensitive and selective urban air pollution monitoring.

Conventional grading in higher education assigns fixed weights to different assessment components, remaining unchanged across time and students, and offering limited flexibility to reflect individual learning trajectories. This study introduces a gamification-inspired assessment design termed Formative-Linked Exam Weighting (FLEX), integrated with Rhythmic Engagement Pedagogy (REP), where each student’s final exam initially carries 100% of the grade and is gradually reduced according to learning-progress points accumulated from formative components, such as quizzes, group tasks, flipped sessions, and inquiry-based milestones. Implemented in an 18-week undergraduate Circuit Theory course, the approach individualized exam weights; by semester’s end, the quartiles were 18.7%, 32.5%, and 44.7%. Compared with fixed-weight formats, FLEX increased voluntary attendance, reduced withdrawals, and improved exam outcomes. Correlation analyses showed that formative engagement aligned with professional-content mastery, REP sustained participation across scales, and FLEX supported learner autonomy. Thus, the proposed assessment provides a scalable framework for enhancing engagement and learning performance. • Formative-Linked Exam Weighting (FLEX) redefines the conventional fixed-weight grading. • FLEX uses internal gamification, converting formative progress into grade rewards. • Rhythmic Engagement Pedagogy (REP) structures alternating learning contrasts. • Combined DGS–REP model sustains engagement and reduces course withdrawals. • The approach enhances mastery, autonomy, and motivation in EMI STEM instruction.