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Gibberellins (GAs) are an important group of phytohormones associated with diverse growth and developmental processes, including cell elongation, seed germination, and secondary growth. Recent genomic and genetic analyses have advanced our knowledge of GA signaling pathways and related genes in model plant species. However, functional genomics analyses of GA signaling pathways in Panax ginseng, a perennial herb, have rarely been carried out, despite its well-known economical and medicinal importance. Here, we conducted functional characterization of GA receptors and investigated their physiological roles in the secondary growth of P. ginseng storage roots. We found that the physiological and genetic functions of P. ginseng gibberellin-insensitive dwarf1s (PgGID1s) have been evolutionarily conserved. Additionally, the essential domains and residues in the primary protein structure for interaction with active GAs and DELLA proteins are well-conserved. Overexpression of PgGID1s in Arabidopsis completely restored the GA deficient phenotype of the Arabidopsis gid1a gid1c (atgid1a/c) double mutant. Exogenous GA treatment greatly enhanced the secondary growth of tap roots; however, paclobutrazol (PCZ), a GA biosynthetic inhibitor, reduced root growth in P. ginseng. Transcriptome profiling of P. ginseng roots revealed that GA-induced root secondary growth is closely associated with cell wall biogenesis, the cell cycle, the jasmonic acid (JA) response, and nitrate assimilation, suggesting that a transcriptional network regulate root secondary growth in P. ginseng. These results provide novel insights into the mechanism controlling secondary root growth in P. ginseng.

Ginseng has some chronic problems caused by the conventional shading structure. Although greenhouse cultivation has been expanded as an affordable alternative solution, no suitable colored woven film (WF), considering the durability, environmental changes, and growth parameters, has yet not been developed. After checking the light reflectance, solar light transmittance, and spectral photon flux distribution of various colored WFs, pearl WF (high light reflectance of 90% and close to 1.0 B/R light ratios) is expected to have positive effects such as increasing growth by manipulating B/R light ratios including temperature reduction due to albedo difference was mass-produced. In this study, 85% two-layered black shade net with general polyethylene film (black PE), blue WF, and pearl WF were covered on the greenhouses to investigate microclimate and growth ginseng. Spectral light through the films indicated that the blue (B) to red (R) light ratios of pearl WF (1.07–1.50) were lower than that of the black PE (0.83–1.69) and blue WF (1.94–8.68) regardless of the weather, whereas the difference in red to far-red light among the films was less than 25%. The air temperature at 2 pm of the pearl WF was 0.6–2.4 o C lower than that of the black PE and blue WF in the summer. The plants grown under the pearl WF had an overall increase in the growth of the aerial part, including the leaf area, more than that of the black PE or blue WF, thereby having the most significant root weight at the time of final harvest. Aside from the targeted product, the total chlorophyll content of the ginseng grown under blue WF was maintained at a high level. The results from the experiment summarized that the pearl WF is recommended to increase the root weight of ginseng because it can ensure durability and provide a cost-effective approach for manipulating microclimate properties to promote their growth.

Physiological responses of ginseng ( Panax ginseng ) were investigated under abnormal season-long high-temperature environmental conditions for obtaining vulnerability assessment data. Soil-plant-atmosphere research chambers were used to employ the + 2, +4, and + 6 elevated temperature conditions (ETC) from June to August compared to hourly-averaged air temperatures for the past 10 years (from 2010 to 2019) in Eumseong, Korea. Under the ETC, secondary growth and development of taproots were significantly inhibited due to the reduction of photosynthetic efficiency with chlorophyll destruction. The net photosynthetic rate at the light saturation point ( A max ) decreased and the dark respiration rate ( R d ) increased as the air temperature increased. Consequently, carbohydrate deposition in the storage parenchyma of the taproots decreased over time. The roots at harvest were severely rotten under + 6 ETC. The harvested root weights decreased by 60.1, 21.4, and 12.3% under + 6, +4, and + 2 ETC, respectively, compared to those under control conditions. Under + 2 and + 4 ETC, total ginsenoside content (TGC) in roots was similar, but under + 6 ETC, TGC significantly increased with the increases of the panaxatriol type ginsenoside Re and the panaxadiol types ginsenosides such as Rb 2 , Rb 3 , and Rd. These results suggest that developing high-temperature stress adaptation technologies should be considered frequent abnormally high-temperature environments caused by global climate change.

As the use of construction robots continues to increase, ensuring safety and productivity while working alongside human workers becomes crucial. To prevent collisions, robots must recognize human behavior in close proximity. However, single, or RGB-depth cameras have limitations, such as detection failure, sensor malfunction, occlusions, unconstrained lighting, and motion blur. Therefore, this study proposes a multiple-camera approach for human activity recognition during human–robot collaborative activities in construction. The proposed approach employs a particle filter, to estimate the 3D human pose by fusing 2D joint locations extracted from multiple cameras and applies long short-term memory network (LSTM) to recognize ten activities associated with human and robot collaboration tasks in construction. The study compared the performance of human activity recognition models using one, two, three, and four cameras. Results showed that using multiple cameras enhances recognition performance, providing a more accurate and reliable means of identifying and differentiating between various activities. The results of this study are expected to contribute to the advancement of human activity recognition and utilization in human–robot collaboration in construction.

Effective monitoring of road fixtures is essential for urban safety and functionality. However, traditional inspections are time-consuming, costly, and error prone, while current automated solutions struggle with high initial setup costs, limited flexibility preventing wide adaptation, and reliance on centralized processing that can delay response times. This study introduces an edge AI-based remote road fixture monitoring system which automatically and continuously updates the information of the road digital twin (DT). The main component is a small-sized edge device consisting of a camera, GPS, and IMU sensors designed to be installed in typical cars. The device captures images, detects the fixture, and estimates their location by employing deep learning and feature matching. This information is transmitted to a dedicated cloud server and represented on a user-friendly user interface. Experiments were conducted to test the system’s performance. The results showed that the device could successfully detect the fixture and estimate their global coordinates. Outputs were marked and shown on the road DT, proving the integrated and smooth operation of the whole system. The proposed Edge AI device demonstrated that it could significantly reduce the data size by 80–84% compared to traditional methods. With a satisfactory object detection accuracy of 65%, the system effectively identifies traffic poles, stop signs, and streetlights, integrating these findings into a digital twin for real-time monitoring. The proposed system improves road monitoring by cutting down on maintenance and emergency response times, increasing the ease of data use, and offering a foundation for an overview of urban road fixtures’ current state. However, the system’s reliance on the quality of data collected under varying environmental conditions suggests potential improvements for consistent performance across diverse scenarios.

PurposeThe study seeks to identify the impact of variables in a deep learning-based bankruptcy prediction model, which has achieved superior performance to other prediction models but cannot easily interpret hidden processes.Design/methodology/approachThis study developed three LSTM-RNN–based models that predicted the probability of bankruptcy before 1, 2 and 3 years using financial, the construction market and macroeconomic variables as input variables. Then, the impacts of the input variables that affected prediction accuracy in each model were identified by using Shapley value and compared among the three models. This study also investigated the prediction accuracy using variants of input variables grouped sequentially by high-impact ranking.FindingsThe results showed that the prediction accuracies were largely impacted by “housing starts” in all models. As the prediction period increased, the effects of macroeconomic variables on prediction accuracy increased, whereas the impact of “return on assets” on prediction accuracy decreased. It also found that the “current ratio” and “debt ratio” significantly influenced the prediction accuracies in all models. Also, the results revealed that similar prediction accuracies could be achieved using only 8, 10, and 10 variables out of a total of 18 variables for the 1-, 2-, and 3-year prediction models, respectively.Originality/valueThis study provides a Shapley value-based approach to identify how each input variable in a deep-learning bankruptcy prediction model. The findings of this study can not only assist in obtaining better insights into the underlying concept of bankruptcy but also use to select variables by removing those identified as less significant.

3D human joint estimation is essential for enabling effective human-robot interaction in construction automation, facilitating precise monitoring of worker movements to enhance safety, ergonomics, and operational efficiency. Vision-based systems utilizing multi-camera setups offer diverse perspectives to address challenges such as occlusions, projection ambiguities, and sensor noise. However, these systems depend heavily on accurate camera calibration to align views and synchronize measurements. Traditional manual calibration methods are timeconsuming, labor-intensive, and prone to human error, making them unsuitable for dynamic construction sites where frequent camera repositioning is required due to shifting conditions and tasks. This study proposes a novel framework that leverages external marker detection for real-time, automated camera calibration, eliminating the need for manual intervention. This approach significantly reduces setup time, minimizes errors, and ensures reliable performance in rapidly changing environments. Furthermore, the framework integrates an Extended Kalman Filter (EKF) to fuse 2D joint locations from multiple cameras, effectively handling sensor noise and the nonlinear nature of human motion. By combining marker-based calibration with EKF-based fusion, the proposed framework delivers a robust and automated solution for 3D human joint estimation, enhancing safety, efficiency, and adaptability in construction automation applications.

The real-time estimation of human activity level in indoor environments is crucial for thermal comfort prediction and optimizing HVAC systems, as well as supporting health monitoring and ergonomic assessments. Existing methods typically categorize activities into predefined types and therefore are not efficient for new activities. Furthermore, the transition between activities is not captured in these classification methods. To address these limitations, we propose a novel Activity Intensity Score (AIS) framework that provides a continuous, non-intrusive assessment of human activity intensity. The proposed AIS framework uses kinematic parameters derived from video-based pose estimation to calculate a dynamic intensity score. Key kinematic parameters, such as angular speed, angular acceleration, range of motion, movement frequency, and rotational energy, are extracted from pose landmarks using MediaPipe. These parameters are then normalized and combined to generate a continuous AIS that reflects real-time variations in movement intensity. The proposed approach was validated through controlled experiments with participants performing activities of varying intensities, including low (e.g., sitting), moderate (e.g., walking), and high (e.g., jumping jacks). Results showed that the AIS effectively distinguishes between different activity intensities, with higher AIS values corresponding to more intensive activities. The proposed method addresses the need for a more granular understanding of activity levels beyond simple categorical classification. This research contributes to real-time activity estimation, which has applications in optimizing indoor environmental conditions, health monitoring, and dynamic ergonomic assessments.

Highway work zones present hazardous conditions where worker safety is at constant risk due to interactions with construction equipment. In such environments, accurately defining hazard zones around equipment is vital for timely detection and prevention of incidents. Traditional static hazard zone models, often relying on simple geometric shapes, fall short as they cannot adapt to changes in equipment speed and movement, leading to either inadequate safety coverage or frequent false alarms. This study proposes a dynamic hazard zone model that leverages a probabilistic approach based on bivariate Gaussian distribution to generate adaptive and realistic hazard zones. Real-time data from GPS and inertial measurement unit (IMU) sensors enable the model to adjust hazard zone dimensions dynamically in response to equipment behavior, improving both accuracy and responsiveness. Simulations conducted in Webots software demonstrated that the proposed model achieved 93% accuracy in hazard detection, significantly outperforming the 60% accuracy of static circular zones tested in the same environment. The findings underscore the potential of dynamic hazard zone modeling to enhance safety in highway work zones, providing a practical and effective approach for reducing risks.