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Social economy and social innovation across diverse contexts: City-level findings from Korea, Italy, and Poland

2026-05-31T15:00:00Z

Title: Social economy and social innovation across diverse contexts: City-level findings from Korea, Italy, and Poland Author(s): Yun, JinHyo Joseph; Zhao, Xiaofei; Koo, Inhyouk; Del Gaudio, Giovanna; Della Corte, Valentina; Turon, Katarzyna; Yigitcanlar, Tan Abstract: This study investigates the contextual foundations that shape the characteristics and dynamics of the social economy and its approach to social open innovation across three countries-South Korea, Italy, and Poland. Specifically, it examines three core dimensions: the agenda pursued, the organizational types involved, and the sustainability of social economy actors. Addressing significant gaps in the literature, the study poses two research questions: (a) How do political, economic, and cultural contexts influence the development of the social economy and social open innovation in each country? (b) In what ways do these contextual differences affect organizational forms, strategic agendas, and long-term sustainability? Employing a comparative, multi-method qualitative approach-including in-depth interviews and participatory observation-the findings reveal stark contrasts across the three countries. Italy's social economy is mature and economically grounded, Poland's is culturally driven yet emergent, and South Korea's is politically shaped but organizationally fragile. These differences suggest that context plays a critical role in defining the evolution, focus, and resilience of the social economy and its potential for fostering socially oriented innovation. The study contributes to grounded theory development by offering a comparative framework that links contextual foundations with the trajectory of social innovation across diverse urban settings.

Entropy-Gated Prediction Agreement for Two-View Video Action Recognition

2026-06-30T15:00:00Z

Title: Entropy-Gated Prediction Agreement for Two-View Video Action Recognition Author(s): Park, Young-Jin; Cho, Hui-Sup Abstract: Human action recognition (HAR) often struggles to capture important temporal cues distributed across an entire video when relying solely on a single sampled clip. To overcome this limitation, this study proposes a framework that constructs two temporal views from the same video and explicitly learns the prediction consistency between them. Specifically, the prediction-level agreement (AG) loss was introduced to align the class probability distributions of the two views. In addition, conditional gating was applied to adaptively control the contribution of AG loss according to the sample-wise prediction confidence, thereby reducing unstable alignment in temporally ambiguous or information-insufficient segments. The proposed framework was evaluated using both convolutional neural network (CNN)- and Transformer-based backbones on three representative action-recognition benchmark datasets, and it generally improved the performance over the single-view baseline across backbone-dataset combinations. Further empirical analyses, including training behavior, motion magnitude, temporal prediction stability, and qualitative case studies, were conducted to examine the effectiveness and behavior of the proposed two-view framework from multiple perspectives.

Rapid detection of airborne fungal contamination using a molecularly imprinted polymer approach for ergosterol

2026-03-31T15:00:00Z

Title: Rapid detection of airborne fungal contamination using a molecularly imprinted polymer approach for ergosterol Author(s): Choi, Eun-Sook; Kim, Jung-Hee; Na, Yun-Cheol; Lee, Bong Gu; Yeo, Min-Kyeong; Kim, Eunjoo Abstract: Fungi are major biological contaminants in indoor air, and their concentration is typically assessed using the culture-based CFU method, which is labor-intensive and time-consuming. Ergosterol, a major fungal cell membrane component, has emerged as a preferred target for alternative analytical approaches. However, ergosterol is highly hydrophobic, and specific affinity probes such as antibodies or aptamers have not yet been successfully developed. In this study, we fabricated ergosterol-specific probes using molecularly imprinted polymers (MIPs) immobilized on carbon nanotubes (CNTs) and integrated them into a screen-printed electrode (SPE) platform. Surface polymerization was initiated through a thiol-ene click reaction using pentaerythritol tetrakis(3-mercaptopropionate) (PETMP) and glyoxal bis(diallyl acetal) (GO), which were selected based on predicted stable conformations for MIP synthesis. The resulting MIP@CNT sensor achieved an imprinting factor (IF) of 19.26 and a limit of detection (LOD) of 0.22 pM for ergosterol. Ergosterol levels in indoor air samples collected on PVC filters were quantified using the MIP@CNT sensor and showed significant correlation with GC/MS measurement (R-2 = 0.5136, p < 0.0001), moderate but statistically significant correlation. This work provides a valuable reference for developing sensing platforms for highly hydrophobic molecules such as sterols and phytosterols, which represent important analytical targets in environmental and biological monitoring.

Stable path planning algorithm for avoidance of dynamic obstacles

2014-12-31T15:00:00Z

Title: Stable path planning algorithm for avoidance of dynamic obstacles Author(s): Kang, Won-Seok; Yun, Sanghun; Kwon, Hyung-Oh; Choi, Rock Hyun; Son, Chang-Sik; Lee, Dong Ha Abstract: Previous research of path planning has focused mainly on finding shortest paths or smallest movements. These methods, however, have poor stability characteristics when dynamic obstacles are considered on real-life or in-body map's environments. In this paper, we suggest a stable path planning algorithm for avoidance of dynamic obstacles. The proposed method makes the movement of a mobile robot more stable in a dynamic environment. Our focus is based on finding optimal movements for stability rather than finding shortest paths or smallest movements. The algorithm is based on Genetic Algorithm (GA) and uses k-means clustering to recognize the distribution of dynamics obstacles in various mobile space. Simulation results confirm this method can determine stable paths through environments involving dynamic obstacles. In order to validate our results, we compared the dynamic k values used in k-means clustering and grid-based dynamic cell sizes from several test sets. © 2015 IEEE.