Given the growing interest of the rubber industry in seeking alternatives that contribute to environmental sustainability, this work aims to present a study of the mechanical, thermal, and structural properties of natural rubber composites using tannin extracted from Acacia mearnsii as an antioxidant agent. Tannin is a natural and biodegradable product, rich in polyphenols and known for its antioxidant properties. The analyses assessed the effectiveness of incorporating tannins (0, 1, 1.5, and 2 parts per hundred rubber) into sulfur-crosslinked natural rubber composites using a binary accelerator system across three distinct vulcanization schemes: conventional, semi-efficient, and efficient. Initially, tannin characterization tests were conducted, revealing characteristic polyphenol bands of proanthocyanidin catechins, a high total phenolic content, and a substantial reduction in antioxidant activity. These findings highlight the significant antioxidant potential of tannins, particularly for industrial and biological applications. The analyses of the characteristics of natural rubber composites with tannin incorporation indicated that the type of vulcanization process directly affects the antioxidant action of the plant tannin, with the tannins being most effective in the efficient system due to the formation of monosulfidic and disulfidic bonds. Furthermore, the incorporation of tannin did not compromise the physical and chemical properties of the materials, highlighting it as a viable additive for the rubber industry.

The need for updated cartographic products in Brazil is essential for effective planning and project implementation. Outdated maps, which can be up to three decades old, hinder various projects, making it necessary to find alternative methods for semi-automated cartographic updates. This study focuses on the use of orbital images obtained through Remote Sensing, processed using Digital Image Processing and Mathematical Morphology techniques. The methodology aims to extract and detect urban targets (allotments) and update an outdated cartographic database. The results obtained demonstrate the achievement of the established objectives, with the proposed methodology outperforming conventional filtering methods. Statistical validation confirms the effectiveness of post-processing techniques such as inpainting. The automated extraction and processing of features using software tools like MATLAB, CARTOMORPH, and QGIS prove to be an efficient solution for updating outdated maps. Ongoing research and refinement of methods aim to enhance the accuracy and reliability of the methodology, making valuable contributions to the cartographic community and benefiting map users worldwide.

Urban sprawl constitutes a significant challenge to sustainable development, particularly in rapidly expanding urban environments. In the case of Guayaquil, in Ecuador, the construction of Perimetral Avenue in 1987 facilitated uncontrolled urban transformation. The absence of effective planning and oversight has led to the conversion of mangrove ecosystems into urban areas in specific sectors, with Isla Trinitaria notably experiencing the most substantial transformation, without yet having a clear strategy or methodology for responding to the problem. Within this framework, the objective of the study is to propose a methodology utilizing free software for optimizing resources in territorial planning. This investigation employed data from MapBiomas and the municipal urban cadastre; spatial information was integrated through free software platforms such as QGIS and PostgreSQL/PostGIS. One of the stages of the methodology included the vectorization of raster images, conducting spatial intersections with the cadastral data, and establishing a geographic database to enable SQL queries. The results indicated urban expansion from 2.1% in 1985 to full occupation in 2010, with a peak in construction activity in 1997. Furthermore, limitations such as low image resolution and data absence due to cloud cover were identified. This proposal aims to enhance sustainable urban planning by leveraging accessible tools, thereby contributing to effective territorial planning and informed decision-making in urban contexts.

The article examines the possibility of using ophthalmic lens waste (OLW) as a filler in styrene–butadiene rubber (SBR) composites in varying proportions. It analyzes the impact of OLW on the composites’ rheological, structural, morphological, mechanical, and thermal properties. Results show that OLW addition does not significantly alter vulcanization time, leading to thermal savings during processing. The crosslink densities, determined by the swelling method in an organic solvent and the mechanical behavior of the elastomers, increased with the incorporation of OLW, suggesting a filler/polymeric matrix interaction. The Lorenz–Parks model confirmed matrix–filler interaction, although it was insufficient to substantially improve mechanical reinforcement, with OLW mainly acting as a filler. Thermogravimetric tests revealed good thermal stability, but dynamic mechanical analysis indicated reduced damping properties. Spectroscopic analysis indicated the lack of molecular bonding between the polymer and the OLW filler. The study suggests that the optimal OLW content is between 10 and 20 phr, enabling the production of a new composite. Overall, incorporating OLW into vulcanized SBR composites offers a sustainable and cost-effective approach to reusing industrial waste in polymer production, providing an environmentally friendly alternative for the polymer industry.

Brazil plays a crucial role in the global economy due to its significant contribution to the agricultural sector, particularly in coffee production, where it stands out as the largest producer and exporter of processed coffee. Various disturbances can influence coffee plants, causing abnormalities that can hinder their successful growth. Parameters such as plant height and canopy diameter play an essential role in assessing the health and productivity of the plants, reflecting their growth, development, and ability to capture sunlight. Additionally, height is also related to the balanced distribution of nutrients and water, providing valuable information about overall performance and the capacity for healthy production. In this regard, the application of methodologies involving remote sensing and machine learning algorithms has shown promising results in the rapid and safe acquisition of information about agricultural systems. This study evaluates different machine learning algorithms, using radiometric values from multispectral images obtained by remote sensing platforms as input datasets for estimating plant height and canopy diameter in coffee cultivation. The best performance was observed for architectures that showed lower RMSE and RMSE% values. For the plant height parameter (m), the RGB sensor exhibited the best performance using the Random Tree algorithm, with an RMSE (0.27) and RMSE% (8.80). For the canopy diameter (m), the sensor showed the best performance using the Random Forest algorithm, with an RMSE (0.15) and RMSE% (8.16).