Predicting tomato water consumption in a hydroponic greenhouse: contribution of light interception models

In recent years, hydroponic greenhouse cultivation has gained increasing popularity: the combination of hydroponics’ highly efficient use of resources with a controlled environment and an extended growing season provided by greenhouses allows for optimized, year-round plant growth. In this direction, precise and effective irrigation management is critical for achieving optimal crop yield while ensuring an economical use of water resources. This study explores techniques for explaining and predicting daily water consumption by utilizing only easily readily available meteorological data and the progressively growing records of the water consumption dataset. In situations where the dataset is limited in size, the conventional purely data-based approaches that rely on statistically benchmarking time series models tend to be too uncertain. Therefore, the objective of this study is to explore the potential contribution of crop models’ main concepts in constructing more robust models, even when plant measurements are not available. Two strategies were developed for this purpose. The first strategy utilized the Greenlab model, employing reference parameter values from previously published papers and re-estimating, for identifiability reasons, only a limited number of parameters. The second strategy adopted key principles from crop growth models to propose a novel modeling approach, which involved deriving a Stochastic Segmentation of input Energy (SSiE) potentially absorbed by the elementary photosynthetically active parts of the plant. Several model versions were proposed and adjusted using the maximum likelihood method. We present a proof-of-concept of our methodology applied to the ekstasis Tomato, with one recorded time series of daily water uptake. This method provides an estimate of the plant’s dynamic pattern of light interception, which can then be applied for the prediction of water consumption. The results indicate that the SSiE models could become valuable tools for extracting crop information efficiently from routine greenhouse measurements with further development and testing. This, in turn, could aid in achieving more precise irrigation management

Three-Generation Super No-Scale Models in Heterotic Superstrings

We derive the conditions for the one-loop contribution to the cosmological constant to be exponentially suppressed in a class of heterotic string compactifications with three generations of chiral matter, where supersymmetry is spontaneously broken \`a la Scherk-Schwarz. Using techniques of partial unfolding based on the Hecke congruence subgroup $\Gamma_0(2)$ of the modular group to extract the leading asymptotics, we show that the super no-scale condition $n_B=n_F$ between the degeneracies of massless bosons $n_B$ and fermions $n_F$ in the full string spectrum is necessary but not sufficient, and needs to be supplemented by additional conditions which we identify. We use these results to construct three-generation Pati-Salam models with interesting phenomenological characteristics.Comment: 17 pages, 2 figure

Cardiometabolic aspects of the polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) is the most common endocrine disorder amongst women of reproductive age and is associated with various metabolic perturbations, in addition to chronic anovulation and factors related to androgen excess. In general, women live longer than men and develop cardiovascular disease at an older age. However, women with PCOS, as compared with age- and body mass index-matched women without the syndrome, appear to have a higher risk of insulin resistance, hyperinsulinemia, glucose intolerance, dyslipidemia, and an increased prothrombotic state, possibly resulting in a higher rate of type 2 diabetes mellitus, fatty liver disease, subclinical atherosclerosis, vascular dysfunction, and finally cardiovascular disease and mortality. Further alterations in PCOS include an increased prevalence of sleep apnea, as well as various changes in the secretion and/or function of adipokines, adipose tissue-derived proinflammatory factors and gut hormones, all of them with direct or indirect influences on the complex signaling network that regulates metabolism, insulin sensitivity, and energy homeostasis. Reviews on the cardiometabolic aspects of PCOS are rare, and our knowledge from recent studies is expanding rapidly. Therefore, it is the aim of the present review to discuss and to summarize the current knowledge, focusing on the alterations of cardiometabolic factors in women with PCOS. Further insight into this network of factors may facilitate finding therapeutic targets that should ameliorate not only ovarian dysfunction but also the various cardiometabolic alterations related to the syndrome