André Luís Brasil Cavalcante

Achieving sustainable infrastructure requires the efficient valorization of regional biomass, such as açaí (Euterpe oleracea) residues. This study provides a multi-scale characterization of untreated and colloidal silica-treated açaí fibers, focusing on their application as sustainable geotechnical reinforcement. The fibers exhibit an average diameter of 0.136 mm and a naturally rough surface densely populated by silica microgranules, favoring mechanical interlocking. Chemical and lignocellulosic analyses (FTIR, XRF, and acid hydrolysis) revealed a high lignin content (45%) and moderate cellulose crystallinity (49%), indicating intrinsic robustness. However, untreated fibers showed high moisture susceptibility, with 80% absorption. The colloidal silica treatment created a stable, silicon-rich protective layer, reducing water absorption by 50% and neutralizing surface acidity, as evidenced by XRF and ash content increase to 17%. Thermogravimetric analysis by TGA/DTG demonstrated enhanced thermal stability and higher char yield for treated fibers, while tensile testing revealed a favorable balance between strength (≈250 MPa) and ductility (7.8% elongation). By bridging the gap between fiber micro-geometry and geotechnical requirements, these results demonstrate that treated açaí residue is a durable, high-value geomaterial for sustainable soil reinforcement in tropical environments.

Objective: To evaluate air quality in different spaces of a public hospital in the Federal District, using environmental parameters such as humidity, temperature, and carbon dioxide, in accordance with ABNT (Brazilian Association of Technical Standards) normative standards. Theoretical Framework: It is based on the principles of Public Health and Health Promotion, articulated with the Sustainable Development Goals (SDG 3 and SDG 11) and the Social Determinants of Health. It also considers guidelines for indoor air quality, hospital management, and ventilation in healthcare services. Method: A cross-sectional observational study was conducted, with data collection at six strategic points in the hospital using the MX1102 Digital Thermo-hygrometer with Data Logger and Carbon Dioxide (CO2) Sensor, manufactured by Onset Computer Corporation, a company specializing in environmental data loggers, which allowed the measurement of environmental conditions. The results were compared with current standards. Results and Discussion: The research indicated that environmental conditions were partially in compliance with standards. In the Surgical Center and ICU, temperature and humidity were adequate, but high CO2 levels suggest the need for improvements in ventilation. In high-traffic areas, high temperatures and low humidity can cause discomfort. As this is an initial study, it is recommended, firstly, to install more efficient ventilation systems and continuously monitor environmental parameters to ensure a safe and comfortable environment. Research Implications: In conclusion, rigorous control of environmental conditions is essential for the safety and well-being of all hospital users and workers. Implementing improvements will reduce the risks of hospital complications, as well as provide a more conducive environment for user recovery, accelerating the discharge process. Originality/Value: The study also suggests the creation of public policies based on continuous monitoring and the adoption of hospital environmental management practices using technologies.

Recent advances in the numerical solution of fractional partial differential equations have yielded promising results. In particular, the Shifted Grünwald–Letnikov (SGL) approach allows for a generalization of the traditional finite difference method to the context of fractional differential equations. However, when combined with the backward finite difference scheme, the SGL method for the fractional advection–dispersion equation can produce spurious results, similar to those observed in its integer‐order counterpart. In this study, a novel method that combines the SGL approach with the Cubic Interpolated Propagation (CIP) denoted as CIP‐SGL scheme is proposed to mitigate numerical dispersion. The accuracy and validity of the proposed method are demonstrated through computational experiments.

Objectives: This study aims to predict slope stability using machine learning algorithms, considering geotechnical and geometric variables to improve the accuracy of safety factor estimation and mitigate risks associated with slope failures. Theoretical Background: Traditional limit equilibrium methods, such as Bishop's method, present limitations when dealing with complex environmental conditions. Recent literature has highlighted the potential of machine learning techniques, such as Random Forest, Neural Networks, and XGBoost, in modeling nonlinear and highly variable systems, which are typical in geotechnical problems. Method: A parametric dataset with 1,520 observations generated through GeoSlope simulations was used, containing variables such as cohesion, internal friction angle, height, slope angle, and water level. Four algorithms were implemented: Random Forest, Gradient Boosting, Artificial Neural Networks, and XGBoost. Model performance was evaluated through cross-validation, hyperparameter tuning, and metrics including R², MSE, and RMSE. Results and Discussion: The XGBoost model achieved the best performance (R² = 0.9841; RMSE = 0.1419), followed by Gradient Boosting and Neural Networks. Variable importance analysis indicated that cohesion and internal friction angle are the primary factors influencing slope stability. Research Implications: The findings reinforce the applicability of machine learning models as decision-support tools in geotechnical engineering projects, offering greater precision and efficiency compared to traditional methods. Originality/Value: This study advances the field by integrating multiple modern algorithms within a replicable modeling framework, demonstrating superior performance over previous approaches and expanding the scope for practical applications in geotechnical engineering.

Unsaturated soil mechanics, when applied to determine the soil shear strength, are crucial for accurately evaluating the safety of geotechnical structures affected by seasonal moisture variations. Over the past decades, multiple models have been formulated to predict the behavior of unsaturated soils in terms of water flow and shear strength individually. Building upon these foundational studies, this research introduces a model that couples an analytical solution for one-dimensional water infiltration with an unsaturated shear strength model. This model further incorporates the impact of void ratio fluctuations on soil properties and state variables related to shear strength. A parametric analysis is conducted to evaluate the effects of the initial void ratio on a representative soil profile during a water infiltration event. The model presented in this paper integrates various concepts from the field of unsaturated soil mechanics and is applicable to any homogeneous soil where expansion/collapse effects are negligible. It demonstrates how shear strength might be underestimated when using a saturated soil approach. Conversely, it may also lead to an overestimation of safety conditions if the soil approaches a saturated or dry state. The proposed model offers a more accurate prediction of unsaturated soil shear strength. It is useful for determining transient safety factors in geotechnical structures. Furthermore, when combined with field-installed instrument monitoring, this model contributes significantly to the functionality, safety, cost-effectiveness, and sustainability of geotechnical structures and projects.

The Landfill Gas (LFG) generated in municipal solid waste (MSW) landfills consists of the main greenhouse gases such as methane (CH₄) and carbon dioxide (CO₂). Cover layer systems of sanitary landfills are designed to mitigate the LFG emission into the atmosphere and control water infiltration. Thus, this research aims to quantify and evaluate CH₄ and CO₂ accurate emissions in the final cover layer system at the Jockey Club Controlled Landfill in Brasília (ACJC) by using the static flux chamber. The emission results were estimated by adjusting the concentration data obtained in 12 tests in the cover layer by the methods of Linear Regression (LR) and the Non-Steady-State Diffusion Flux Estimator (NDFE). Fugitive emissions ranged from 0 to 685.6 g CH₄ m^-2 day^-1 and 0 to 1,563.94 g CO₂ m^-2 day^-1 using the LR method. Using the NDFE method, emissions up to 4.2 and 2.5 times greater than the LR method were estimated for CH₄ and CO₂, respectively.