Data-driven crop growth simulation on time-varying generated images using multi-conditional generative adversarial networks.

Background: Image-based crop growth modeling can substantially contribute to precision agriculture by revealing spatial crop development over time, which allows an early and location-specific estimation of relevant future plant traits, such as leaf area or biomass. A prerequisite for realistic and sharp crop image generation is the integration of multiple growth-influencing conditions in a model, such as an image of an initial growth stage, the associated growth time, and further information about the field treatment. While image-based models provide more flexibility for crop growth modeling than process-based models, there is still a significant research gap in the comprehensive integration of various growth-influencing conditions.

[Spectrum of Minimally Invasive Surgical Techniques for the Managment of Inguinal Hernias in Infants and Children].

One of the most common surgical procedures in infants and children is the repair of an indirect inguinal hernia. This can be carried out by open technique or using minimally invasive surgery (MIS). Since 1998, numerous different MIS techniques have been described.

Validation of a Field-Portable, Handheld Real-Time PCR System for Detecting Pseudogymnoascus destructans, the Causative Agent of White-Nose Syndrome in Bats.

White-nose syndrome (WNS), caused by the fungus Pseudogymnoascus destructans, has decimated bat populations across North America. Despite ongoing management programs, WNS continues to expand into new populations, including in US states previously thought to be free from the pathogen and disease. This expansion highlights a growing need for surveillance tools that can be used to enhance existing monitoring programs and support the early detection of P.

Recognizing protected and anthropogenic patterns in landscapes using interpretable machine learning and satellite imagery.

The accurate and comprehensive mapping of land cover has become a central task in modern environmental research, with increasing emphasis on machine learning approaches. However, a clear technical definition of the land cover class is a prerequisite for learning and applying a machine learning model. One of the challenging classes is naturalness and human influence, yet mapping it is important due to its critical role in biodiversity conservation, habitat assessment, and climate change monitoring.