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Hyperhydricity (HH) is one of the most important physiological disorders that negatively affects various plant tissue culture techniques. The objective of this study was to characterize optical features to allow an automated detection of HH. For this purpose, HH was induced in two plant species, apple and Arabidopsis thaliana, and the severity was quantified based on visual scoring and determination of apoplastic liquid volume. The comparison between the HH score and the apoplastic liquid volume revealed a significant correlation, but different response dynamics. Corresponding leaf reflectance spectra were collected and different approaches of spectral analyses were evaluated for their ability to identify HH-specific wavelengths. Statistical analysis of raw spectra showed significantly lower reflection of hyperhydric leaves in the VIS, NIR and SWIR region. Application of the continuum removal hull method to raw spectra identified HH-specific absorption features over time and major absorption peaks at 980 nm, 1150 nm, 1400 nm, 1520 nm, 1780 nm and 1930 nm for the various conducted experiments. Machine learning (ML) model spot checking specified the support vector machine to be most suited for classification of hyperhydric explants, with a test accuracy of 85% outperforming traditional classification via vegetation index with 63% test accuracy and the other ML models tested. Investigations on the predictor importance revealed 1950 nm, 1445 nm in SWIR region and 415 nm in the VIS region to be most important for classification. The validity of the developed spectral classifier was tested on an available hyperspectral image acquisition in the SWIR-region.
SimBO is a flexible framework for optimizing discrete event-driven simulations (DES) using sequential optimization algorithms. While specifically designed for Bayesian Optimization (BO) in the context of DES, SimBO can be applied to any black-box problem with other optimization algorithms. The framework consists of four encapsulated components - the black-box problem, the sequential optimization algorithm, a database for experiment configuration and results, and a web-based graphical user interface - that communicate via well-defined interfaces. Each component can be run in different environments, allowing for cooperation between different hardware- and software configurations. In our research context, SimBO’s architecture enabled BO algorithms to be run on a high-performance cluster with GPU support, while the simulation is executed on a local Windows machine using the Simio simulation software. The framework’s flexibility also makes it suitable for evolving from a research-focused tool to a production-ready, cloud-based optimization tool for modern algorithms.
The excitement sparked by the emergence of AI open platforms has encountered significant scrutiny from educators and educational planners, who have raised valid concerns about issues such as plagiarism, testing protocols, and the authenticity of content submitted by students. While these concerns are timely and crucial, it's essential not to overlook other pressing issues that often go unnoticed in the lived educational experience of learners, particularly within the field of social sciences. This paper aims to advocate for a humanistic approach with a focus on education in the generative AI Era.
Einleitung
Die Prävalenz der über 80-jährigen bei Ulcus cruris venosum (VLU) beträgt 4-5 %, obwohl diese Altersgruppe nur 1 % der Gesamtbevölkerung ausmacht. Zusätzlich wird bei VLU-Patienten häufig eine Mangelernährung beobachtet. Insbesondere geriatrische Patienten leiden darunter. Dabei ist bekannt, dass Mangelernährung Einfluss auf die Wundheilung und somit auf die Lebensqualität der Patienten hat. Diverse Studien beschreiben erste erfolgreiche ernährungstherapeutische Ansätze für einen beschleunigten Wundheilungsprozess. Allerdings ist die Ernährungstherapie bei VLU-Patienten wenig erforscht. Ziel dieser Arbeit ist es einen Überblick über den ernährungsphysiologischen Einfluss zur VLU zu schaffen, um mögliche Ernährungsinterventionen für geriatrische Patienten zu erhalten.
Grasslands are ubiquitous globally, and their conservation and restoration are critical to combat both the biodiversity and climate crises. There is increasing interest in implementing effective multifunctional grassland restoration to restore biodiversity concomitant with above- and belowground carbon sequestration, delivery of carbon credits and/or integration with land dedicated to solar panels. Other common multifunctional restoration considerations include improved forage value, erosion control, water management, pollinator services, and wildlife habitat provisioning. In addition, many grasslands are global biodiversity hotspots. Nonetheless, relative to their impact, and as compared to forests, the importance of preservation, conservation, and restoration of grasslands has been widely overlooked due to their subtle physiognomy and underappreciated contributions to human and planetary well-being. Ultimately, the global success of carbon sequestration will depend on more complete and effective grassland ecosystem restoration. In this review, supported by examples from across the Western world, we call for more strenuous and unified development of best practices for grassland restoration in three areas of concern: initial site conditions and site preparation; implementation of restoration measures and management; and social context and sustainability. For each area, we identify the primary challenges to grassland restoration and highlight case studies with proven results to derive successful and generalizable solutions.