Significance of AI-assisted techniques for epiphyte plant monitoring and identification from drone images.

Monitoring forest canopies is vital for ecological studies, particularly for assessing epiphytes in rain forest ecosystems. Traditional methods for studying epiphytes, such as climbing trees and building observation structures, are labor, cost intensive and risky. Unmanned Aerial Vehicles (UAVs) have emerged as a valuable tool in this domain, offering botanists a safer and more cost-effective means to collect data. This study leverages AI-assisted techniques to enhance the identification and mapping of epiphytes using UAV imagery. The primary objective of this research is to evaluate the effectiveness of AI-assisted methods compared to traditional approaches in segmenting/identifying epiphytes from UAV images collected in a reserve forest in Costa Rica. Specifically, the study investigates whether Deep Learning (DL) models can accurately identify epiphytes during complex backgrounds, even with a limited dataset of varying image quality. Systematically, this study compares three traditional image segmentation methods Auto Cluster, Watershed, and Level Set with two DL-based segmentation networks: the UNet and the Vision Transformer-based TransUNet. Results obtained from this study indicate that traditional methods struggle with the complexity of vegetation backgrounds and variability in target characteristics. Epiphyte identification results were quantitatively evaluated using the Jaccard score. Among traditional methods, Watershed scored 0.10, Auto Cluster 0.13, and Level Set failed to identify the target. In contrast, AI-assisted models performed better, with UNet scoring 0.60 and TransUNet 0.65. These results highlight the potential of DL approaches to improve the accuracy and efficiency of epiphyte identification and mapping, advancing ecological research and conservation.

Cognitive Distortions and Suicide Attempts.

Although theorists have posited that suicidal individuals are more likely than non-suicidal individuals to experience cognitive distortions, little empirical work has examined whether those who recently attempted suicide are more likely to engage in cognitive distortions than those who have not recently attempted suicide. In the present study, 111 participants who attempted suicide in the 30 days prior to participation and 57 psychiatric control participants completed measures of cognitive distortions, depression, and hopelessness. Findings support the hypothesis that individuals who recently attempted suicide are more likely than psychiatric controls to experience cognitive distortions, even when controlling for depression and hopelessness. Fortune telling was the only cognitive distortion uniquely associated with suicide attempt status. However, fortune telling was no longer significantly associated with suicide attempt status when controlling for hopelessness. Findings underscore the importance of directly targeting cognitive distortions when treating individuals at risk for suicide.