Constantly growing environmental concerns focused on reducing pollution, in addition to rising fuel costs in recent years, have led the maritime industry to develop and implement fuel-saving solutions. Among them is the optimization of marine propeller efficiency, as marine propellers are a crucial part of ship’s propulsion system. During the initial design stage, selecting the optimal propeller is considered a multi-objective optimization process. This research focused on maximizing propeller open water efficiency, while minimizing engine brake power constrained by thrust and cavitation. Optimization was applied to Wageningen B-series propellers and conducted using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). The algorithm selected optimum parameters to create the propeller model, which was then evaluated numerically through computational fluid dynamics (CFD) with a multiple reference frame (MRF) and under the SST k-ω turbulence model, to obtain the open water hydrodynamic characteristics. In addition, the cavitation effect was evaluated using the Zwart–Gerber–Belamri cavitation model. The numerical model results were validated through comparison with open water experimental data from the Netherlands Ship Model Basin for the Wageningen B-series propellers. The results showed errors of 3.29% and 2.01% in efficiency under noncavitating and cavitating conditions, respectively. Correct performance of the functions was shown, based on neural networks trained to estimate thrust and torque coefficients instead of polynomials. The proposed optimization process and numerical model are suitable for solving multi-objective optimization problems in the preliminary design of fixed-pitch marine propellers.

The rapid growth of urban areas has increased the need for more efficient methods of pavement inspection and maintenance. However, conventional techniques remain slow, labor-intensive, and limited in spatial coverage, and their performance is strongly affected by traffic, weather conditions, and operational constraints. In response to these challenges, it is essential to synthesize the technological advances that improve inspection efficiency, coverage, and data quality compared to traditional approaches. Herein, we present a systematic review of the state of the art on the use of unmanned aerial vehicles (UAVs) for monitoring and assessing pavement deterioration, highlighting as a key contribution the comparative integration of sensors (photogrammetry, LiDAR, and thermography) with recent automatic damage-detection algorithms. A structured review methodology was applied, including the search, selection, and critical analysis of specialized studies on UAV-based pavement evaluation. The results indicate that UAV photogrammetry can achieve sub-centimeter accuracy (<1 cm) in 3D reconstructions, LiDAR systems can improve deformation detection by up to 35%, and AI-based algorithms can increase crack-identification accuracy by 10% to 25% compared with manual methods. Finally, the synthesis shows that multi-sensor integration and digital twins offer strong potential to enhance predictive maintenance and support the transition towards smarter and more sustainable urban infrastructure management strategies.

The objective of this study is to develop a genetic algorithm that uses the IGP parameterization to increase the lift coefficient (C_L) of three airfoils to be used on wings of unmanned aerial vehicles (UAVs). The geometry of three baseline airfoils was modified by developing a genetic algorithm that operates with the IGP parameterization and performs the aerodynamic analysis using XFOIL in the MATLAB environment. Subsequently, a numerical model was made for each baseline and optimized airfoil using a commercial computational fluid dynamics (CFD) code to analyze the behavior of the lift coefficient. An increase in the average C_L was obtained for the Eppler 68, MH 70, and Wortmann FX 60-126 airfoils for angles of attack ranging from 0 to 10, obtaining increments of 17.243%, 14.967%, and 10.708%, respectively. Additionally, an average 5.027% uncertainty was obtained in lift coefficient calculations between XFOIL and CFD. The utility of the IGP method and genetic algorithms for parameterizing and optimizing airfoils was demonstrated. In addition, airfoils could be tailored for a specific UAV depending on the mission profile. Volume 2 of this study will include experimental data from wind tunnel.

Premature deterioration of concrete structures in coastal areas requires a careful evaluation based on durability criteria. Electrical Resistivity (ER) serves as a valuable indicator of concrete durability, as it reflects how easily aggressive agents can penetrate its pores. This testing method offers several advantages; it is non-destructive, rapid, and more cost-effective than the chloride permeability test (RCPT). Furthermore, durable concrete typically necessitates larger quantities of cement, which contradicts the goals of sustainable concrete development. Thus, a significant challenge is to create concrete that is both durable and sustainable. This research explores the effects of pozzolanic additives, specifically Volcanic Ash (VA) and Sugarcane Bagasse Ash (SCBA), on the electrical resistivity of eco-friendly concretes exposed to the coastal conditions of the Gulf of Mexico. The electrical resistivity (ER) was measured at intervals of 3, 7, 14, 21, 28, 45, 56, 90, and 180 days across 180 cylinders, each with dimensions of 10 cm × 20 cm. The sustainability of the concrete was evaluated based on its energy efficiency. Three types of mixtures were developed using the ACI 211.1 method, maintaining a water-to-cement (w/c) ratio of 0.57 with CPC 30 R RS cement and incorporating various additions: (1) varying percentages of VA (2.5%, 5%, and 7.5%), (2) SCBA at rates of 5%, 10%, and 15%, and (3) ternary mixtures featuring VA-SCBA ratios of 1:1, 1:2, and 1:3. The findings indicated an increase in ER of up to 37% and a reduction in CO₂ emissions ranging from 4.2% to 16.8% when compared to the control mixture, highlighting its potential for application in structures situated in aggressive environments.

The construction sector is responsible for approximately 38% of global CO₂ emissions, driven primarily by energy demand for thermal comfort. This research evaluated the implementation of a Passive Climate Control System (PCCS) based on circular economy principles. This system acts as a thermal buffer composed of recycled PET bottles filled with 500 mL of water and a reflective coating, installed on the roof slab. Through a case–control study in Veracruz, Mexico, hygrothermal performance was monitored for ten months. The results demonstrated the superiority of the PCCS over the conventional slab: in autumn–winter, temperature fluctuations were reduced by 26.7%, while in spring–summer, the maximum temperature was limited by 1.3 °C during the daytime peak. Fractal analysis confirmed that the PCCS promotes a homogeneous thermal distribution (Fd ≤ 1.255), maintaining internal conditions within the comfort zone. The implementation of this PCCS, based on waste valorization, is a robust and sustainable solution that offers a viable alternative to active climate control. It promotes hygrothermal comfort and reduces energy consumption, aligning with the objectives of Clean Technology and building efficiency.