Browsing by Subject "11 Ciudades y comunidades sostenibles"

We have conducted an entropy analysis in Alaska, a seismic-rich region in a subduction zone that exhibits a nontrivial behavior: the subduction arc alters the seismic activity from the eastern zone to the western zone, demonstrating a decrease in activity along the subduction. We analyze this zone through the Tsallis entropy and the mutability (or dynamic entropy) for the first time. Considering 13 870 seismic events after appropriate filtering, we analyzed a data set for the selected Alaska zone between 2000 and 2023. We have found agreement between the results for the two entropies. We have followed the value of the q parameter of the Tsallis entropy (Sq) finding values between 1.70 and 1.85, in concordance with values found in other seismic regions of the planet. The values of Sq decrease slightly over time but show a broad increase before the major earthquakes. Just opposite to Tsallis entropy, mutability shows a tendency to decrease prior to the major earthquakes. We used the simpler mutability method to further analyze this zone upon dividing the region into four subzones. The results show how mutability can identify the seismic activity in each zone. This study shows how an entropy approach can shed light on understanding the seismicity in subduction zones.

In medium- and low-voltage three-phase distribution networks, the load imbalance among the phases may compromise the network voltage symmetry. Inverter-interfaced distributed energy resources (DERs) can contribute to compensating for such imbalances by sharing the required negative sequence current while providing active power synchronized with the positive sequence voltage. However, positive and negative sequences are conventionally defined in a steady state and are not directly observed from the instantaneous voltage and current measurements at the DER unit’s point of connection. In this article, an estimation algorithm for sequence separation based on the Kalman filter is proposed. Furthermore, the proposed filter uses a complex vector representation of the asymmetric three-phase signals in synchronous coordinates to allow for the implementation of the Kalman filter in its stationary form, resulting in a simple dynamic filter able to estimate positive and negative sequences even during transient operation. The proposed stationary complex Kalman filter performs better than state-of-the-art techniques like DSOGI and very similarly to other Kalman filter implementations found in the literature but at a fraction of its computational cost (23.5%).

Although walking methodologies (WMs) and machine learning (ML) have been objects of interest for urban scholars, it is difficult to find research that integrates both. We propose a ‘cyborg walk’ method and apply it to studying litter in public spaces. Walking routes are created based on an unsupervised learning algorithm (k-means) to classify public spaces. Then, a deep learning model (YOLOv5) is used to collect data from geotagged photos taken by an automatic Insta360 X3 camera worn by human walkers. Results from image recognition have an accuracy between 83.7% and 95%, which is similar to what is validated by the literature. The data collected by the machine are automatically georeferenced thanks to the metadata generated by a GPS attached to the camera. WMs could benefit from the introduction of ML for informative route optimisation and georeferenced visual data quantification. The links between these findings and the existing WM literature are discussed, reflecting on the parallels between this ‘cyborg walk’ experiment and the seminal cyborg metaphor proposed by Donna Haraway.

This paper introduces a novel approach based on matrix multiplication in 𝔽𝑛×𝑛𝑝, which enables methods for public key exchange, user authentication, digital signatures, blockchain integration, and homomorphic encryption. Unlike traditional algorithms that rely on integer factorization or discrete logarithms, our approach utilizes matrix factorization, rendering it resistant to current quantum cryptanalysis techniques. This method enhances confidentiality by ensuring secure communication and facilitating user authentication through public key validation. We have incorporated a method that allows a Certification Authority to certify the public keys. Furthermore, the incorporation of digital signatures ensures nonrepudiation, while the system functions as a blockchain technology to enhance transaction security. A key innovation of this approach is its capability to perform homomorphic encryption. Our approach has practical applications in artificial intelligence, robotics, and image processing.

This article introduces a novel systematic methodology for modeling a class of multidimensional linear mechanical systems that directly allows to obtain their infinite-dimensional port-Hamiltonian representation. While the approach is tailored to systems governed by specific kinematic assumptions, it encompasses a wide range of models found in current literature, including ℓ-dimensional elasticity models (where ℓ = 1, 2, 3), vibrating strings, torsion in circular bars, classical beam and plate models, among others. The methodology involves formulating the displacement field using primary generalized coordinates via a linear algebraic relation. The non-zero components of the strain tensor are then calculated and expressed using secondary generalized coordinates, enabling the characterization of the skew-adjoint differential operator associated with the port-Hamiltonian representation. By applying Hamilton's principle and employing a specially developed integration by parts formula for the considered class of differential operators, the port-Hamiltonian model is directly obtained, along with the definition of boundary inputs and outputs. To illustrate the methodology, the plate modeling process based on Reddy's third-order shear deformation theory is presented as an example. To the best of our knowledge, this is the first time that a port-Hamiltonian representation of this system is presented in the literature.

This study presents a benchmark of two physics-guided neural surrogates – PhyCNN and multi–LSTM – across three single-degree-of-freedom system scenarios of increasing nonlinearity: (I) a Duffing oscillator; (II) a hysteretic Bouc–Wen system under band-limited noise; and (III) a hysteretic Bouc–Wen isolator representative of base-isolated buildings subjected to long duration Chilean earthquakes. Physics-guided LSTMs achieve the lowest errors in displacement and velocity and better reproduce hysteretic geometry; CNNs yield tighter, more stable errors when the normalized internal force is predicted directly. Reconstructing force from LSTM velocity closes much of the gap but remains fragile in long, strongly nonlinear records due to residual-drift accumulation. Simple training discipline—input/target normalization, validation-driven early stopping and scheduling, and light force regularization—substantially improves robustness.

Los edificios tradicionales de entramado de madera representan una parte importante del patrimonio arquitectónico chileno. En particular, la tipología conocida como tabique-adobillo se propagó por localidades como Valparaíso, Santiago, Coquimbo, y el campamento minero Sewell. En este sistema las uniones carpinteras son cruciales para dar estabilidad a la estructura, ya que son las encargadas de transmitir las cargas entre sus elementos y disipar la energía durante un sismo, lo que las vuelve vulnerables a daños por sobrecarga y a la separación de las piezas. Reforzar las uniones permite mejorar la capacidad y estabilidad de las estructuras, previniendo dicha separación. Esta investigación propone una metodología semicuantitativa para el diseño de soluciones de reforzamiento de uniones carpinteras, integrando la teoría de resolución de problemas de inventiva y los principios modernos de rehabilitación. La metodología propuesta se aplica para diseñar dos refuerzos para la unión caja y espiga, característica de la tipología tabique-adobillo.