Search for: All records

A catastrophic Mw7.8 earthquake hit southeast Turkey and northwest Syria on February 6th, 2023, leading to more than 44 k deaths and 160 k building collapses. The interpretation of earthquake-triggered building damage is usually subjective, labor intensive, and limited by accessibility to the sites and the availability of instant, high-resolution images. Here we propose a multi-class damage detection (MCDD) model enlightened by artificial intelligence to synergize four variables, i.e., amplitude dispersion index (ADI) and damage proxy (DP) map derived from Synthetic Aperture Radar (SAR) images, the change of the normalized difference built-up index (NDBI) derived from optical remote sensing images, as well as peak ground acceleration (PGA). This approach allows us to characterize damage on a large, tectonic scale and a small, individual-building scale. The integration of multiple variables in classifying damage levels into no damage, slight damage, and serious damage (including partial or complete collapses) excels the traditional practice of solely use of DP by 11.25% in performance. Our proposed approach can quantitatively and automatically sort out different building damage levels from publicly available satellite observations, which helps prioritize the rescue mission in response to emergent disasters.

 

Global climate models (GCMs) and Earth system models (ESMs) exhibit biases, with resolutions too coarse to capture local variability for fine-scale, reliable drought and climate impact assessment. However, conventional bias correction approaches may cause implausible climate change signals due to unrealistic representations of spatial and intervariable dependences. While purely data-driven deep learning has achieved significant progress in improving climate and earth system simulations and predictions, they cannot reliably learn the circumstances (e.g., extremes) that are largely unseen in historical climate but likely becoming more frequent in the future climate (i.e., climate non-stationarity). This study shows an integrated trend-preserving deep learning approach that can address the spatial and intervariable dependences and climate non-stationarity issues for downscaling and bias correcting GCMs/ESMs. Here we combine the super-resolution deep residual network (SRDRN) with the trend-preserving quantile delta mapping (QDM) to downscale and bias correct six primary climate variables at once (including daily precipitation, maximum temperature, minimum temperature, relative humidity, solar radiation, and wind speed) from five state-of-the-art GCMs/ESMs in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We found that the SRDRN-QDM approach greatly reduced GCMs/ESMs biases in spatial and intervariable dependences while significantly better-reducing biases in extremes compared to deep learning. The estimated drought based on the six bias-corrected and downscaled variables captured the observed drought intensity and frequency, which outperformed state-of-the-art multivariate bias correction approaches, demonstrating its capability for correcting GCMs/ESMs biases in spatial and multivariable dependences and extremes.

 

Microbial culture collections play a crucial role in the collection, maintenance, and distribution of quality-assured living microbial strains, along with their associated phenotypic and omics data. To enhance the find-able, accessible, interoperable, and re-usable (FAIR) data utilization of microbial resources, the World Data Center for Microorganisms (WDCM) has developed the Global Catalogue of Microorganisms (GCM) and the Global Catalogue of Type Strains (gcType). These platforms provide interactive interfaces for cataloging the holdings of collections, along with detailed annotations of type strain genomes and curated metadata, including ecosystems, growth conditions, and collection locations. The system maximizes the scientific impact of microbial resources and culture collections through an integrated data mining tool that links strain- and species-related information from various public resources. Currently, the GCM and gcType include 574 422 strains from 154 culture collections across 51 countries and regions, along with 25 980 genomes from type species. Additionally, 2 702 655 articles and 103 337 patents are integrated with these microbial resources. The system supports microbial taxonomic research and provides evidence for implementing the Nagoya Protocol in the field of microbial resources and their digital sequence information (DSI). Access is freely available at gcm.wdcm.org and gctype.wdcm.org.

 

This research investigates fatigue’s impact on arm gestures within augmented reality environments. Through the analysis of the gathered data, our goal is to develop a comprehensive understanding of the constraints and unique characteristics affecting the performance of arm gestures when individuals are fatigued. Based on our findings, prolonged engagement in full-arm movement gestures under the influence of fatigue resulted in a decline in muscle strength within upper body segments. Thus, this decline led to a notable reduction in the accuracy of gesture detection in the AR environment, dropping from an initial 97.7% to 75.9%. We also found that changes in torso movements can have a ripple effect on the upper and forearm regions. This valuable knowledge will enable us to enhance our gesture detection algorithms, thereby enhancing their precision and accuracy, even in fatigue-related situations.

 

This study examines the ergonomic impact of augmented reality (AR) technologies in educational contexts, with a focus on understanding how prolonged AR engagement affects postural dynamics and physical demands on users. By analyzing slouching scores alongside NASA Task Load Index (TLX) Physical Demand (PD) values, we assess the physical strain experienced by participants during the initial modules of an AR-based lecture series. Our findings demonstrate a notable decline in slouching scores as participants progress through the lecture modules, indicating increased postural deviations. To quantify these effects, we developed a regression model that effectively predicts the physical demands imposed by various AR modules, based on the observed slouching scores.

 

Inquiry-based course experiences provide a scalable and equitable way to engage students in research. In this study, we describe how we introduced inquiry-based experiences to ten lower-division and upper-division courses across the biology curriculum at a regionally comprehensive public university serving the diverse population in a major metropolitan area. Student survey data suggest this redesign effectively developed students’ scientific skills and nurtured their sense of belonging. This project illustrates how inquiry-based experiences can be implemented sustainably across institutional context.

 

This research aims to explore the prediction of student learning outcomes in Augmented Reality (AR) educational settings, focusing on engineering education, by analyzing pupil dilation and problem-solving time as key indicators. In this research, we have created an innovative AR learning platform through the incorporation of eye-tracking technology into the Microsoft HoloLens 2. This enhanced learning platform enables the collection of data on pupil dilation and problem-solving duration as students engage in AR-based learning activities. In this study, we hypothesize that pupil dilation and problem-solving time could be significant predictors of student performance in the AR learning environment. The results of our study suggest that problem-solving time may be a critical factor in predicting student learning success for materials involving procedural knowledge at low difficulty levels. Additionally, both pupil dilation and problem-solving time are predictive indicators of student learning outcomes when dealing with predominantly procedural knowledge at high difficulty levels.

 

Augmented Reality (AR) technology offers the possibility of experiencing virtual images with physical objects and provides high quality hands-on experiences in an engineering lab environment. However, students still need help navigating the educational content in AR environments due to a mismatch problem between computer-generated 3D images and actual physical objects. This limitation could significantly influence their learning processes and workload in AR learning. In addition, a lack of student awareness of their learning process in AR environments could negatively impact their performance improvement. To overcome those challenges, we introduced a virtual instructor in each AR module and asked a metacognitive question to improve students’ metacognitive skills. The results showed that student workload was significantly reduced when a virtual instructor guided students during AR learning. Also, there is a significant correlation between student learning performance and workload when they are overconfident. The outcome of this study will provide knowledge to improve the AR learning environment in higher education settings.