Integration of functional and traditional food in emerging markets. Regulatory and substantive aspects of yerba mate and quinoa

Given the rising cost of healthcare, the increase in life expectancy and the wish for a better quality of life, the request for foods and beverages producing a beneficial effect on health has increased worldwide. “Functional food” is a new concept and may play a key role in diseases’ prevention and management. Although its meaning is currently under definition, its role in global health improvement is growing constantly. This article aims at giving a description of existing legislation on functional food in South America, identifying future directions for health and marketing policies. Furthermore, authors provide a literature revision on two products widely consumed in Latin American countries: Yerba Mate and Quinoa. Thanks to their beneficial health effects in terms of disease prevention and promotion of well-being, they may be considered as functional foods with a potential key role in health care

Ropivacaine vs tetracaine in topical anesthesia for intravitreal injection

Aim: The object of the study was to evaluate the long term efficacy and safety of ropivacaine 0,5% vs tetracaine 0,5% for topical anesthesia in intravitreal injection of dexamethasone in patients with diabetic macular edema (DME) and anti-vascular endothelial growth factor (VEGF) therapy. Methods: Thirty-seven patients were enrolled in the study. Intravitreal anti-vascular endothelial growth factor (VEGF) and Dexamethasone were placed in DME patients. Intravitreal administration determines appropriate and long-lasting drug's concentration without systemic side effects. Topical anesthesia under ropivacaine 0,5% vs tetracaine 0,5% was performed. Results: Intravitreal injection without any supplemental anesthesia and sedation was realized. Patients reported mild pain (recorded by a 0 to 10 scale) during the procedure with optimal operative result. Conclusions: Topical anesthesia with ropivacaine and tetracaine is safe and effective in intravitreal injection. The long-lasting anesthesia secured low pain during this limited but unpleasant procedure

A first order phase transition with non-constant density

AbstractWe introduce a new model for first order phase transitions accounting for non-constant densities of the phases during the process. The resulting initial and boundary value problem for a PDE system is recovered by thermodynamical principles. The resulting system presents some singularities and strong nonlinearities accounting for internal constraints, ensuring in particular the positivity of the pressure and the temperature. Physical consistency for the order parameter comes from a maximum principle argument. Existence of a weak solution is proved by a regularization-passage to the limit procedure

Generating a Dataset for Comparing Linear vs. Non-Linear Prediction Methods in Education Research

Machine learning is often used to build predictive models by extracting patterns from large data sets. Such techniques are increasingly being utilized to predict outcomes in the social sciences. One such application is predicting student success. Machine learning can be applied to predicting student acceptance and success in academia. Using these tools for education-related data analysis, may enable the evaluation of programs, resources and curriculum. Currently, research is needed to examine application, admissions, and retention data in order to address equity in college computer science programs. However, most student-level data sets contain sensitive data that cannot be made public. To help facilitate research and the application of machine learning models to this field, we generate an artificial student-level data set of 50,000 students to simulate college admissions data. We generate this data set for public access and without privacy concerns. Once the data is generated, we then analyze it using logistic regression, K-Nearest Neighbor, random forest, neural networks, and XGBoost techniques to demonstrate and compare the type of analyses that can be conducted on data sets of this type. Finally we provide an analysis on whether the predictive gains of machine learning models outweigh the potential loss of interpretability in comparison to classical statistical methods

Statistical Study of Uncontrolled Geostationary Satellites Near an Unstable Equilibrium Point

The growth of the population of space debris in the geostationary ring and the resulting threat to active satellites require insight into the dynamics of uncontrolled objects in the region. A Monte Carlo simulation analyzed the sensitivity to initial conditions of the long-term evolution of geostationary spacecraft near an unstable point of the geopotential, where irregular behavior (e.g., transitions between long libration and continuous circulation) occurs. A statistical analysis unveiled sudden transitions from order to disorder, interspersed with intervals of smooth evolution. There is a periodicity of approximately half a century in the episodes of disorder, suggesting a connection with the precession of the orbital plane, due to Earth's oblateness and lunisolar perturbations. The third-degree harmonics of the geopotential also play a vital role. They introduce an asymmetry between the unstable equilibrium points, enabling the long libration mode. The unpredictability occurs just in a small fraction of the precession cycle, when the inclination is close to zero. A simplified model, including only gravity harmonics up to degree 3 and the Earth and Moon in circular coplanar orbits is capable of reproducing most features of the high-fidelity simulation

Nucleon form factors in a simple three-body quark model

We construct a simple 3-body quark model for the non strange nucleon resonances and we give results for the spectrum, the helicity amplitudes and the transition form factors. All the observables, in particular the transition form factors, are evaluated analytically and the results are compared with those of other models