The cambucizeiro (Campomanesia phaea), belonging to the Myrtaceae family, is a native plant of the Brazilian Atlantic Forest. The description of the characteristics of the cambucizeiro fruits is important to support new genetic improvement works and its commercial exploitation, especially regarding the processing of the fruit. The present work aimed to perform the morphological and chemical characterization of the cambucizeiro fruits. Fifty-eight accessions, from different locations in the Atlantic Forest and Serra do Mar in the state of São Paulo, were collected, propagated by seeds and one specimen of each accessory is at the Seedling Production Center in São Bento do Sapucaí (SP). Forty fruits from each access were collected in May and submitted to the following analyses: longitudinal and transversal diameter, total fruit fresh mass, number and mass of seeds, total soluble solids, % citric acid, ratio, firmness, vitamin C and coloration. Fruit conformation varies intensely among accessions. The number of seeds is not a good indicator for the relation with the fruit mass, but the mass of one thousand seeds. Some accessions have high soluble solids content, but, on the other hand, the vast majority have fruits with high acidity. Cambuci is an excellent source of vitamin C. The fruits of the accessions are green in color, persisting an opaque shade when ripe.

In order to improve the quality and efficiency of heat treatment in welds of power stations, this paper summarizes the current situation of 600 MW supercritical power plant welding site heat treatment and puts forward the improved methods and measures accordingly. The heat treatment of welding holes in the construction site Play a certain guiding role.

The Organic Rankine Cycle (ORC) is an electricity generation system that uses organic fluid instead of water in the low temperature range. The Organic Rankine cycle using zeotropic working fluids has wide application potential. In this study, data mining (DM) model is used for performance analysis of organic Rankine cycle (ORC) using zeotropik working fluids R417A and R422D. Various DM models, including Linear Regression (LR), Multi-Layer Perceptron (MLP), M5 Rules, M5 Model Tree, Random Committee (RC), and Decision Tree (DT) models are used. The MLP model emerged as the most effective approach for predicting the thermal efficiency of both R417A and R422D. The MLP’s predicted results closely matched the actual results obtained from the thermodynamic model using Genetron software. The Root Mean Square Error (RMSE) for the thermal efficiency was exceptionally low, at 0.0002 for R417A and 0.0003 for R422D. Additionally, the R-squared (R²) values for thermal efficiency were very high, reaching 0.9999 for R417A and R422D. The findings demonstrate the effectiveness of the DM model for complex tasks like estimating ORC thermal efficiency. This approach empowers engineers with the ability to predict thermal efficiency in organic Rankine systems with high accuracy, speed, and ease.

This paper provides a comprehensive review of SURF (speeded up robust features) feature descriptor, commonly used technique for image feature extraction. The SURF algorithm has obtained significant popularity because to its robustness, efficiency, and invariance to various image transformations. In this paper, an in-depth analysis of the underlying principles of SURF, its key components, and its use in computer vision tasks such as object recognition, image matching, and 3D reconstruction are proposed. Furthermore, we discuss recent advancements and variations of the SURF algorithm and compare it with other popular feature descriptors. Through this review, the aim is to provide a clear understanding of the SURF feature descriptor and its significance in the area of computer vision.