An aero-structural algorithm to optimize a flying wing in cruise conditions for preliminary design is developed using two-way interaction between the structure and aerodynamics. A particle swarm routine is employed to solve the multi-objective optimization, aiming to reduce the weight of the structure and the aerodynamic drag at the design point. Different shapes are evaluated during the optimization process until the algorithm reaches the optimal wing aspect ratio, taper ratio, angle of incidence, twist angle, swept angle, and airfoil shape, where a six-parameters method is employed to allow reflex airfoils. A main isotropic I-beam models the wing structure. An extended vortex lattice model is employed to model the aerodynamics, along with a high-order panel method with fully coupled viscous interaction. The finite element method is used to solve the flying-wing structure under static loads. An algorithm is developed to iterate between the deflection of the wing and its impact on the aerodynamics until convergence is reached. Different constraints are implemented into the objective function to fulfil the structural criteria and the longitudinal static stability. A comparison against a baseline optimization is carried out, achieving higher efficiency and promising results in elliptical lift distribution, and a high static margin, without the use of non-constant twist. The results suggest that combining both reflex airfoils and sweep with washout is the optimal solution to reduce the drag and weight, keeping the longitudinal static stability criteria for tailless aircraft in the lower end of the transonic regime.

Wildfires pose significant threats to tropical ecosystems, yet, fire precursors in these regions are not well understood. This study conducts a statistical analysis of fire precursors in the tropical area of Antioquia, Colombia, using remote sensing data from VIIRS and MODIS satellites. We integrated and pre-processed data on fire occurrences, the Enhanced Vegetation Index (EVI), land classifications, and various weather and soil variables to ensure compatibility for statistical analysis. Our findings reveal that reductions in EVI by 3.9% and relative humidity by 21%, coupled with increases in air temperature by 4.9 °C, soil temperature by 2.77 °C, and wind speed by 0.17 m/s, are significant precursors to fire occurrences in this tropical region. Notably, precipitation and soil moisture did not show definitive correlations with fire events, contrasting with findings in temperate regions. Spatial and temporal analyses indicate that fires are more frequent during the first three months of the year and tend to recur in specific areas, suggesting persistent environmental risks and human influences, particularly in built-up areas. The limitations of the study include reliance on a single land classification dataset from 2020, which may not account for land cover changes over the study period, and the coarse temporal resolution of EVI data that could overlook rapid vegetation changes. Despite these limitations, the results provide valuable insights for enhancing early warning systems, informing policy development, and improving resource allocation for fire risk management in tropical ecosystems.

Even though some methods for the detection of colorectal cancer have been used clinically, most of the techniques used do not consider the in situ detection of colorectal cancer (CRC) biomarkers, which would favor in vivo real-time monitoring of the carcinogenesis process and consequent studies of the disease. In order to give a scientific and computational framework ideal for the evaluation of diagnosis techniques based on the early detection of biomarker molecules modeled as spherical particles from the computational point of view, a computational representation of the rectum, stool and biomarker particles was developed. As consequence of the transport of stool, there was a displacement of CRC biomarker particles that entered the system as a result of the cellular apoptosis processes in polyps with a length lower than 1 cm, reaching a maximum velocity of $3.47\times {10}^{-3}$ m/s. The biomarkers studied showed trajectories distant to regions of the polyp of origin in 1 min of simulation. The research results show that the biomarker particles for CRC respond to the variations in the movements of the stool with trajectories and speeds that depend on the location of the injury, which will allow locating the regions with the highest possibilities of catching particles through in situ measurement instruments in the future.

The aim behind principal component pursuit is to recover a low-rank matrix and a sparse matrix from a noisy signal which is the sum of both matrices. This optimization problem is a priori and non-convex and is useful in signal processing, data compression, image processing, machine learning, fluid dynamics, and more. Here, a distributed scheme described by a static undirected graph, where each agent only observes part of the noisy or corrupted matrix, is applied to achieve a consensus; then, a robust approach that can also handle missing values is applied using alternating directions to solve the convex relaxation problem, which actually solves the non-convex problem under some weak assumptions. Some examples of image recovery are shown, where the network of agents achieves consensus exponentially fast.