A recent study published in Automation in Construction by researchers from Incheon National University exploits a novel approach to improving excavator tracking performance under real-world conditions. By integrating deep learning-based instance segmentation with an automated, reliability-based multi-camera strategy, this study addresses one of the most persistent challenges in construction monitoring—frequent occlusions caused by dynamic site activities. In addition, the researchers propose a frame-level reliability estimation process that automatically identifies unreliable tracking results.

A field study reveals that combining biochar with organic fertilizer can significantly improve soil structure, nutrient availability, and beneficial microbial communities in dryland farming systems, offering a promising pathway toward more sustainable agriculture.

A new method developed by researchers at Koç University enables users to join online platforms through invitations without revealing who invited whom, protecting sensitive social connections from potential privacy risks.

A comprehensive review highlights how modified biochar is rapidly emerging as a powerful tool for improving soil health, boosting crop productivity, and supporting climate-smart agriculture. At the same time, the study warns that inconsistent performance and limited understanding of soil interactions still pose challenges for widespread adoption.

Seoul National University College of Engineering announced that a research team led by Prof. Changbum R. Ahn of the Department of Architecture and Architectural Engineering has developed a behavior-based safety assessment system capable of measuring construction workers’ ability to perceive risks and respond appropriately to them, in collaboration with Prof. Jeongmi Lee’s team at the Graduate School of Culture Technology at KAIST. This system utilizes three tests grounded in cognitive psychology and, unlike conventional safety knowledge assessments that rely on quizzes or image interpretation, evaluates workers’ actual, real-world behavioral characteristics—including attention, speed of risk recognition, and inhibitory control. The system has attracted attention from both academia and industry and is expected to contribute to accident prevention at construction sites and to the development of customized safety training tailored to individual cognitive traits. The research findings were recently published in the international journal, Journal of Safety Research, in the fields of safety engineering and safety science.

Study confirms public observations match professional records for common felid species, guiding better conservation monitoring.