Abstract
Quadruped robots hold immense potential for navigating in unknown environments due to their ability to use individual footholds as well as their increased stability in uneven terrain. However, legged robots often experience limitations due to weight shifts during gait transitions. These weight shifts can cause torque peaks that exceed the capacity of the jointmotors (overdrive torque), which lead to an increased risk of mechanical failure. Through the optimization of gait parameters, it is possible to reduce these risks while maximizing performance. This paper presents the use of multi-objective optimization algorithms for gait optimization in a simulated quadruped mammal robot within the Pybullet physics engine. The main focus of the study was to compare the performance of NSGA-II, NSGA-III and U-NSGA-III in minimizing overdrive torque while maximizing travel distance. The results showed that the three algorithms solve this problem, although the NSGA-III consistently yields better results in comparison to the other versions of the NSGA algorithm.

Abstract
Understanding and accurately predicting stomatal conductance in almond orchards is critical for effective water-management strategies, especially under challenging climatic conditions. In this study, machine-learning (ML) regression models trained on multispectral (MSP) and thermal infrared (TIR) data acquired from unmanned aerial vehicles (UAVs) are used to address this challenge. Through an analysis of spectral indices calculated from UAV-based data and feature-selection methods, this study investigates the predictive performance of three ML models (extra trees, ET; stochastic gradient descent, SGD; and extreme gradient boosting, XGBoost) in predicting stomatal conductance. The results show that the XGBoost model trained with both MSP and TIR data had the best performance (R2 = 0.87) and highlight the importance of integrating surface-temperature information in addition to other spectral indices to improve prediction accuracy, up to 11% more when compared to the use of only MSP data. Key features, such as the green-red vegetation index, chlorophyll red-edge index, and the ratio between canopy temperature and air temperature (Tc-Ta), prove to be relevant features for model performance and highlight their importance for the assessment of water stress dynamics. Furthermore, the implementation of Shapley additive explanations (SHAP) values facilitates the interpretation of model decisions and provides valuable insights into the contributions of the features. This study contributes to the advancement of precision agriculture by providing a novel approach for stomatal conductance prediction in almond orchards, supporting efforts towards sustainable water management in changing environmental conditions.

Abstract
In the capitalist rush to attract more visitors, cities are committing significant resources to heritage conservation, driven by the substantial economic benefits generated by the tourism industry. However, less famous or less well-resourced cities, often with smaller populations, also known as intermediary cities, find it difficult to allocate funds to protect their most significant heritage sites. In this conservation context, intermediary cities, often on the periphery or 'at the margins', can fill the gaps and needs of urbanism through a better strategic understanding of the challenges of global touristification, thus this research provides urban planning tools for local governments with limited resources to preserve their architectural heritage through remote sensing, for its advantages in terms of lower economic cost, as a valuable monitoring tool to effectively identify high-vulnerability sites that require priority attention in the conservation of architectural heritage. In other words, it allows for a reduction in the territory of those areas located 'at the margins' in terms of urban planning and management, by approaching the territorial, urban, architectural and tourism problems from a transdisciplinary perspective in the preservation of the architectural heritage. This study explores the application of optical (Sentinel-2) using neural networks for classifying the land cover and radar (Sentinel-1 and PAZ) satellite images to obtain the ground motion as a geotechnical risk study, together with geospatial data, for the monitoring of architectural heritage in intermediate cities. Focusing on the districts of Bragan & ccedil;a and Guarda in Portugal, the approach allows the direct identification of vulnerable architectural heritage, identifying 9 highly-vulnerable areas using PAZ data and 7 areas using Sentinel-1 data. Furthermore, this work provides an understanding of the potential and limitations of these technologies in heritage preservation because compares the processing results of freely accessible medium-resolution Sentinel-1 radar imagery with the high-resolution radar images from the innovative PAZ satellite.

Abstract
The United Nations has set Sustainable Development Goals (SDGs) to build a more sustainable future. The SDG analyzes progress to understand major implementation challlenges, define disparities across nations or regions, and propose priorities for action. It has 17 objectives and more than 200 indicators. Cluster analysis was used to categorize the 10 municipalities. It was carried out using IBM SPSS software, which calculated the Euclidean distance and put the investigated regions into clusters with the traits they shared the most in common.

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