Non-Critical Covariant Superstrings

We construct a covariant description of non-critical superstrings in even dimensions. We construct explicitly supersymmetric hybrid type variables in a linear dilaton background, and study an underlying N=2 twisted superconformal algebra structure. We find similarities between non-critical superstrings in 2n+2 dimensions and critical superstrings compactified on CY_(4-n) manifolds. We study the spectrum of the non-critical strings, and in particular the Ramond-Ramond massless fields. We use the supersymmetric variables to construct the non-critical superstrings sigma-model action in curved target space backgrounds with coupling to the Ramond-Ramond fields. We consider as an example non-critical type IIA strings on AdS_2 background with Ramond-Ramond 2-form flux.Comment: harvmac, amssym, 46 p

From Industrial Food Waste to Bioactive Ingredients: A Review on the Sustainable Management and Transformation of Plant-Derived Food Waste

According to the United Nations, approximately one-third of the food produced for human consumption is wasted. The actual linear "Take-Make-Dispose" model is nowadays obsolete and uneconomical for societies and the environment, while circular thinking in production systems and its effective adoption offers new opportunities and benefits. Following the "Waste Framework Directive" (2008/98/CE), the European Green Deal, and the actual Circular Economy Action Plan, when prevention is not possible, recovering an unavoidable food waste as a by-product represents a most promising pathway. Using last year's by-products, which are rich in nutrients and bioactive compounds, such as dietary fiber, polyphenols, and peptides, offer a wake-up call to the nutraceutical and cosmetic industry to invest and develop value-added products generated from food waste ingredients

Animal Models for Periodontal Disease

Animal models and cell cultures have contributed new knowledge in biological sciences, including periodontology. Although cultured cells can be used to study physiological processes that occur during the pathogenesis of periodontitis, the complex host response fundamentally responsible for this disease cannot be reproduced in vitro. Among the animal kingdom, rodents, rabbits, pigs, dogs, and nonhuman primates have been used to model human periodontitis, each with advantages and disadvantages. Periodontitis commonly has been induced by placing a bacterial plaque retentive ligature in the gingival sulcus around the molar teeth. In addition, alveolar bone loss has been induced by inoculation or injection of human oral bacteria (e.g., Porphyromonas gingivalis) in different animal models. While animal models have provided a wide range of important data, it is sometimes difficult to determine whether the findings are applicable to humans. In addition, variability in host responses to bacterial infection among individuals contributes significantly to the expression of periodontal diseases. A practical and highly reproducible model that truly mimics the natural pathogenesis of human periodontal disease has yet to be developed

Compact Measurement Station for Low Energy Proton Beams

A compact, remote controlled, cost efficient diagnostic station has been developed to measure the charge, the profile and the emittance for low energy proton beams. It has been installed and tested in the proton beam line of the Project Prometheus at SANAEM of the Turkish Atomic Energy Authority.Comment: 7 pages 2 column

Neural Network Complexity of Chaos and Turbulence

We study the complexity of chaos and turbulence as viewed by deep neural networks by considering network classification tasks of distinguishing turbulent from chaotic fluid flows, noise and real world images of cats or dogs. We analyze the relative difficulty of these classification tasks and quantify the complexity of the computation at the intermediate and final stages. We analyze incompressible as well as weakly compressible fluid flows and provide evidence for the feature identified by the neural network to distinguish turbulence from chaos

Turbulence Scaling from Deep Learning Diffusion Generative Models

Complex spatial and temporal structures are inherent characteristics of turbulent fluid flows and comprehending them poses a major challenge. This comprehesion necessitates an understanding of the space of turbulent fluid flow configurations. We employ a diffusion-based generative model to learn the distribution of turbulent vorticity profiles and generate snapshots of turbulent solutions to the incompressible Navier-Stokes equations. We consider the inverse cascade in two spatial dimensions and generate diverse turbulent solutions that differ from those in the training dataset. We analyze the statistical scaling properties of the new turbulent profiles, calculate their structure functions, energy power spectrum, velocity probability distribution function and moments of local energy dissipation. All the learnt scaling exponents are consistent with the expected Kolmogorov scaling and have lower errors than the training ones. This agreement with established turbulence characteristics provides strong evidence of the model's capability to capture essential features of real-world turbulence

The efficiency of fan-pad cooling system in greenhouse and building up of internal greenhouse temperature map

During summer periods, high temperature values that are being formed in greenhouses can greatly influence the efficiency of production workers and also decrease the productivity of plants grown there. A greenhouse production without the cooling systems can be sustained at the desirable level by imposing summer restrictions in the areas with warm climate, and by starting cooling in the areas with cold climate. A statement can be made regarding both utility and efficiency of fan-pad cooling systems that they tend to go up in the areas with low relative air humidity. The present study has been carried out in order to either prove or disprove this statement. We have attempted to create a map of internal greenhouse temperature distribution via determining the system’s efficiency. As a result of this study, it was determined that since air temperature and relative humidity in the air tend to decrease during summer months by using fan-pad cooling system, temperatures in the greenhouse can be consequently lowered down to 10-12°C. Statistical analysis revealed remarkable differences (