On a tropicalization of planar polynomial ODEs with finitely many structurally stable phase portraits

Recently, concepts from the emerging field of tropical geometry have been used to identify different scaling regimes in chemical reaction networks where dimension reduction may take place. In this paper, we try to formalize these ideas further in the context of planar polynomial ODEs. In particular, we develop a theory of a tropical dynamical system, based upon a differential inclusion, that has a set of discontinuities on a subset of the associated tropical curve. The development is inspired by an approach of Peter Szmolyan that uses the connection of tropical geometry with logarithmic paper. In this paper, we define a phaseportrait, a notion of equivalence and characterize structural stability. Furthermore, we demonstrate the results on several examples, including a(n) (generalized) autocatalator model. Our main result is that there are finitely many equivalence classes of structurally stable phase portraits and we enumerate these ($15$ in total) in the context of the generalized autocatalator model. We believe that the property of finitely many structurally stable phase portraits underlines the potential of the tropical approach, also in higher dimension, as a method to obtain and identify skeleton models in chemical reaction networks in extreme parameter regimes

Poems 1933 (selection) – Translated by Katherine Cassis

Poems 1933 (selection) – Translated by Katherine Cassi

Autonomous greenhouse for the indoors based on a microcontroller

Περίληψη: Ο σκοπός της παρούσας εργασίας είναι η μελέτη, ο σχεδιασμός και η υλοποίηση ενός πλήρως αυτόματου θερμοκηπίου εσωτερικού χώρου. Το θερμοκήπιο θα αξιολογεί και θα μεταβάλλει τις περιβαλλοντικές συνθήκες, όπως είναι η υγρασία, ο φωτισμός και η θερμοκρασία, με στόχο την μεγιστοποίηση της ανάπτυξης των φυτών. Δίνεται η δυνατότητα δημιουργίας ενός ασύρματου δικτύου θερμοκηπίων. Το κάθε θερμοκήπιο θα μπορεί να φιλοξενεί διαφορετικές οικογένειες φυτών. Ο χρήστης θα παρατηρεί και θα μεταβάλλει τις συνθήκες στο κάθε θερμοκήπιο μέσω Internet. Επίσης μελλοντικά θα συνδέεται με εφαρμογή για έξυπνα τηλέφωνα.Summarization: The objective of this research is to design and implement a fully automated indoor greenhouse. This greenhouse will monitor and adjust the levels of humidity, lighting and temperature in order to maximize plants’ growth. A special feature of the designed greenhouse is its ability to connect wirelessly and to create a network of multiple greenhouses. Thereby, each individual greenhouse may contain plants of the same family, growing under different conditions. Users are also able to interact with each greenhouse and modify its settings through the internet. The next versions will allow users to monitor their plants via their smart phones

Non-Destructive Early Detection and Quantitative Severity Stage Classification of Tomato Chlorosis Virus (ToCV) Infection in Young Tomato Plants Using Vis–NIR Spectroscopy

Tomato chlorosis virus (ToCV) is a serious, emerging tomato pathogen that has a significant impact on the quality and quantity of tomato production worldwide. Detecting ToCV via means of spectral measurements in an early pre-symptomatic stage offers an alternative to the existing laboratory methods, leading to better disease management in the field. In this study, leaf spectra from healthy and diseased leaves were measured with a spectrometer. The diseased leaves were subjected to RT-qPCR for the detection and quantification of the titer of ToCV. Neighborhood component analysis (NCA) algorithm was employed for the feature selection of the effective wavelengths and the most important vegetation indices out of the 24 that were tested. Two machine learning methods, namely XY-fusion network (XY-F) and multilayer perceptron with automated relevance determination (MLP–ARD), were employed for the estimation of the disease existence and viral load in the tomato leaves. The results showed that before outlier elimination, the MLP–ARD classifier generally outperformed the XY-F network with an overall accuracy of 92.1% against 88.3% for the XY-F. Outlier elimination contributed to the performance of the classifiers as the overall accuracy for both XY-F and MLP–ARD reached 100%

DRAM1 promotes lysosomal delivery of mycobacterium marinum in macrophages

Animal science