The Federal University of Vales do Jequitinhonha e Mucuri (Portuguese: Universidade Federal dos Vales do Jequitinhonha e Mucuri, UFVJM) is a public university in Diamantina, Minas Gerais, established 1953..
The cooling stage is the main bottleneck in charcoal production using masonry kilns, as it can account for several days of downtime before kiln unloading and directly limits process productivity. The objective of this study is to develop and validate a Computational Fluid Dynamics (CFD) model capable of predicting the natural cooling stage of masonry charcoal kilns. The proposed model accounts for coupled heat transfer by conduction, natural convection, and radiation. The charcoal bed is modeled as a packed bed permeated by pyrolysis gases. Heat transfer between the charcoal particles and the gases in the bed is accounted for by adding a source term to the energy equation. The kiln exchanges heat with the environment by natural convection and radiation. To avoid explicitly simulating the carbonization stage, an initial temperature field is reconstructed from experimental measurements at the onset of cooling. Model predictions for a two dimensional geometry are validated against full-scale experimental data obtained from a rectangular masonry kiln, with a total of ten temperature measurements distributed in the charcoal bed, gas region, and kiln walls. All temperatures predicted by the model have a mean absolute percentage error below 6.1%. The best performance is achieved for wall temperatures, with mean absolute percentage errors below 3% and an R2 of 0.98. A sensitivity analysis indicates that kiln cooling is primarily governed by the thermal capacity of the walls and external heat transfer resistances, whereas gas-bed heat transfer parameters have a secondary influence. The proposed CFD framework provides an important tool for analyzing the natural cooling of masonry kilns and offers a basis for the design and optimization of technologies aimed at reducing cooling time and increasing charcoal production productivity.
Coffee fruit drying is an essential step, as it directly affects the drink’s final quality. The presence of green fruits in this process significantly reduces the quality standard. Conventional characterization techniques are performed manually, are time-consuming, and lead to increased estimation errors. Thus, the objective was to identify green, red, and black fruits in the drying patio by digital classification of multispectral and RGB images. The data were obtained by remotely piloted aircraft during daily flights over coffee fruits for eight days of drying. The analyses involved color segregation techniques, supervised classification (Random Forest), and the application of thirty vegetation indices to RGB and multispectral images, utilizing Python, RStudio, and GIS to generate statistical models and maps, in conjunction with manual fruit estimation and monitoring of temperature and humidity. The use of aerial images proved effective in monitoring colors only during the first three days, due to rapid moisture loss. In all analyses, green fruits showed better spectral separation. Image segmentation proved to be the superior method, adequate up to the third day. Random Forest showed reduced performance due to shadows and color mixing and was only effective on the first day. Among the RGB indices, ExGR and MGRVI stood out, with high potential for detecting green fruits, while TGI indicated two classes of green, highlighting the efficiency of conventional sensors. For multispectral images, NDVI was effective on the first day, and NDWI was effective up to the third day, influenced by fruit of humidity reduction. The need to develop a specific vegetative index to standardize this monitoring is emphasized, based on green separation, considering humidity and robust quality indicators.
This systematic review aimed to evaluate the effects of strength exercise dosages on pain and disability in individuals with low back pain. Systematic review of randomized controlled trials. MEDLINE, PEDro, EMBASE, Cochrane Library, AMED, and PsycINFO. Randomized controlled trials comparing strength exercises of any dosage with minimal interventions for pain and disability in individuals with low back pain of any duration. Two reviewers independently screened trials, extracted data, assessed quality, and evaluated evidence using the GRADE framework. Mean differences with 95 https://doi.org/10.17605/OSF.IO/D34TJ .
Snakebite envenomation is a neglected tropical disease with a significant impact on public health, especially in socially vulnerable regions. Our objective was to describe the clinical and epidemiological profile of cases and identify factors associated with severity in a Brazilian mesoregion. A retrospective ecological study was conducted using secondary data from the Notifiable Diseases Information System, including all reported cases of snakebite envenomation between 2014 and 2024. Sociodemographic, clinical, and healthcare-related variables were analyzed. Severity was classified as mild, moderate, or severe according to national guidelines. Multinomial logistic regression was performed to assess factors associated with severity. Missing data were handled using multiple imputation by chained equations. 1,272 cases were recorded, corresponding to an incidence of 346.87 cases per 100,000 inhabitants. Most cases occurred in males (69.5
Leaf herbivory is a ubiquitous ecological interaction that varies significantly in intensity across species, habitats, and biogeographic regions. Although quantification of leaf damage is crucial for understanding many ecological processes, the accuracy and precision of various damage estimation methods used by researchers, including visual estimation, digital image analysis, and artificial intelligence, have not been evaluated and compared. We use a phylogenetically diverse group of tropical plants to compare the accuracy and precision of damage estimation methods and use the results to provide a guide to herbivory estimation that balances the advantages and disadvantages of each method. We found that visual estimation tended to overestimate herbivory levels compared to digital methods but was 15 times faster and improved in accuracy and speed with training. Conversely, deep-learning algorithms underestimated herbivory relative to image analysis with ImageJ when it was on the margin, but showed similar accuracy for damage inside of leaf margins. Our results indicate that while visual methods allow for rapid assessment of large sample sizes and are suitable for detecting broad patterns of damage, image analysis is crucial for accurate and precise quantification. The disadvantages of each method, however, can be minimized through proper training and efficient use of each tool, and we therefore provide a guide of practical approaches to herbivory estimation.
