A feasibility cachaca type recognition using computer vision and pattern recognition

Brazilian rum (also known as cachaça) is the third most commonly consumed distilled alcoholic drink in the world, with approximately 2.5 billion liters produced each year. It is a traditional drink with refined features and a delicate aroma that is produced mainly in Brazil but consumed in many countries. It can be aged in various types of wood for 1-3 years, which adds aroma and a distinctive flavor with different characteristics that affect the price. A research challenge is to develop a cheap automatic recognition system that inspects the finished product for the wood type and the aging time of its production. Some classical methods use chemical analysis, but this approach requires relatively expensive laboratory equipment. By contrast, the system proposed in this paper captures image signals from samples and uses an intelligent classification technique to recognize the wood type and the aging time. The classification system uses an ensemble of classifiers obtained from different wavelet decompositions. Each classifier is obtained with different wavelet transform settings. We compared the proposed approach with classical methods based on chemical features. We analyzed 105 samples that had been aged for 3 years and we showed that the proposed solution could automatically recognize wood types and the aging time with an accuracy up to 100.00% and 85.71% respectively, and our method is also cheaper.info:eu-repo/semantics/publishedVersio

Pharmacotherapy review: a proposal to improve medication adherence among hypertensive patients

Abstract Pharmacotherapy review is a structured assessment of medicines, which aims to obtain a partnership with patients to achieve drug treatment goals and agreement about drug dosage, as well as when and how the drugs should be administered. The objective was to analyze the influence of pharmacotherapy review, by scheduling drug administration to improve medication adherence among antihypertensive patients. This study was an uncontrolled intervention developed in three distinct stages. The first stage included data collection on the profile of patients and their medications, and a preliminary assessment of medication adherence. In the second stage, the review report was delivered to patients. In the third stage, the results of blood pressure and medication adherence were assessed. The influence of the revision was measured through statistical tests (p<0.05). The study included 40 patients with a mean age of 58.0 (SD:11.3) years; 72.5% were women. Thirty-three (82.5 %) patients required some intervention, after when there was a significant increase in the number of daily doses (p=0.039) and drug intakes (p=0.025). There was a significant increase in the adherence rate, according to both the Morisky-Green test (p<0.001) and self-reported assessment (p=0.004). There was also an improvement in the levels of systolic (p<0.001) and diastolic (p=0.002) blood pressure and in the number of patients with controlled hypertension (p=0.006). The pharmaceutical service enhanced medication adherence and control of systemic blood pressure; however, it increased the complexity of treatment

Fitting the integrated Spectral Energy Distributions of Galaxies

Fitting the spectral energy distributions (SEDs) of galaxies is an almost universally used technique that has matured significantly in the last decade. Model predictions and fitting procedures have improved significantly over this time, attempting to keep up with the vastly increased volume and quality of available data. We review here the field of SED fitting, describing the modelling of ultraviolet to infrared galaxy SEDs, the creation of multiwavelength data sets, and the methods used to fit model SEDs to observed galaxy data sets. We touch upon the achievements and challenges in the major ingredients of SED fitting, with a special emphasis on describing the interplay between the quality of the available data, the quality of the available models, and the best fitting technique to use in order to obtain a realistic measurement as well as realistic uncertainties. We conclude that SED fitting can be used effectively to derive a range of physical properties of galaxies, such as redshift, stellar masses, star formation rates, dust masses, and metallicities, with care taken not to over-interpret the available data. Yet there still exist many issues such as estimating the age of the oldest stars in a galaxy, finer details ofdust properties and dust-star geometry, and the influences of poorly understood, luminous stellar types and phases. The challenge for the coming years will be to improve both the models and the observational data sets to resolve these uncertainties. The present review will be made available on an interactive, moderated web page (sedfitting.org), where the community can access and change the text. The intention is to expand the text and keep it up to date over the coming years.Comment: 54 pages, 26 figures, Accepted for publication in Astrophysics & Space Scienc

Simulating maize yield in sub-tropical conditions of southern Brazil using Glam model

The objective of this work was to evaluate the feasibility of simulating maize yield in a sub‑tropical region of southern Brazil using the general large area model (Glam). A 16‑year time series of daily weather data were used. The model was adjusted and tested as an alternative for simulating maize yield at small and large spatial scales. Simulated and observed grain yields were highly correlated (r above 0.8; p<0.01) at large scales (greater than 100,000 km2), with variable and mostly lower correlations (r from 0.65 to 0.87; p<0.1) at small spatial scales (lower than 10,000 km2). Large area models can contribute to monitoring or forecasting regional patterns of variability in maize production in the region, providing a basis for agricultural decision making, and Glam‑Maize is one of the alternatives