Terraced Landscapes and Hydrological-Geological Hazards: Innovative Approaches and Future Perspectives

In the last few years, terraced landscapes have been receiving renovated interest all over the world (e [...

Optimal Design and Control of 4-IWD Electric Vehicles based on a 14-DOF Vehicle Model

A 4-independent wheel driving (4-IWD) electric vehicle has distinctive advantages with both enhanced dynamic and energy efficiency performances since this configuration provides more flexibilities from both the design and control aspects. However, it is difficult to achieve the optimal performances of a 4-IWD electric vehicle with conventional design and control approaches. This work is dedicated to investigating the vehicular optimal design and control approaches, with a 4-IWD electric race car aiming at minimizing the lap time on a given circuit as a case study. A 14-DOF vehicle model that can fully evaluate the influences of the unsprung mass is developed based on Lagrangian dynamics. The 14-DOF vehicle model implemented with the reprogrammed Magic Formula tire model and a time-efficient suspension model supports metric operations and parallel computing, which can dramatically improve the computational efficiency. The optimal design and control problems with design parameters of the motor, transmission, mass center, anti-roll bar and the suspension of the race car are successively formulated. The formulated problems are subsequently solved by directly transcribing the original problems into large scale nonlinear optimization problems based on trapezoidal approach. The influences of the mounting positions of the propulsion system, the mass and inertia of the unsprung masses, the anti-roll bars and suspensions on the lap time are analyzed and compared quantitatively. Some interesting findings that are different from the `already known facts' are presented

Continuous Monitoring of Transmission Lines Sag through Angular Measurements Performed with Wireless Sensors

High voltage transmission lines are crucial infrastructure that are demanded to supply an increasing request of electric energy. In the design and operations stages, sag represents a key parameter which must respect specific constraints. Therefore, sag continuous monitoring is becoming essential to guarantee the correct functioning of the line and to optimize the current flow. Different solutions have been proposed in literature, but they are still lacking efficiency and reliability to be used during operations. In this work, a simple and efficient method, based on conductor parabolic approximation, is developed and used to compute the sag through the measurement of the conductor slope in proximity of the span extremities. The angular measurements are obtained using wireless sensors equipped with MEMS accelerometers developed by authors and employed for HVTL conductor vibration monitoring. The proposed method and its implementation in the monitoring system was tested in a laboratory environment on a real conductor. The values of sag at different tensile loads have been obtained and compared to the measured ones, with satisfactory results according to the accelerometer resolution. The solution developed therefore represents a complete and innovative tool to be adopted in the field to monitor, in real time, both the sag and the level of vibration due to the wind action, allowing to increase the performance reliability of HVTL

Parametrizing GP Trees for Better Symbolic Regression Performance through Gradient Descent [Poster]

Pietropolli, G., Camerota verdù, F. J., Manzoni, L., & Castelli, M. (2023). Parametrizing GP Trees for Better Symbolic Regression Performance through Gradient Descent [Poster]. In S. Silva, & L. Paquete (Eds.), GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationJuly 2023 (pp. 619-622). Association for Computing Machinery (ACM). https://doi.org/10.1145/3583133.3590574Symbolic regression is a common problem in genetic programming (GP), but the syntactic search carried out by the standard GP algorithm often struggles to tune the learned expressions. On the other hand, gradient-based optimizers can efficiently tune parametric functions by exploring the search space locally. While there is a large amount of research on the combination of evolutionary algorithms and local search (LS) strategies, few of these studies deal with GP. To get the best from both worlds, we propose embedding learnable parameters in GP programs and combining the standard GP evolutionary approach with a gradient-based refinement of the individuals employing the Adam optimizer. We devise two different algorithms that differ in how these parameters are shared in the expression operators and report experimental results performed on a set of standard real-life application datasets. Our findings show that the proposed gradient-based LS approach can be effectively combined with GP to outperform the original algorithm.publishersversionpublishe

A review of minimally invasive fracture stabilization in dogs and cats

Objective: To summarize and discuss peer-reviewed studies on minimally invasive osteosynthesis (MIO) of long bone, physeal, and articular fractures in dogs and cats. Study design: Invited review. Methods: A critique of literature was performed to assess MIO feasibility, outcomes, and complications through PubMed, Scopus, and CAB abstracts research databases (2000-2020). Results: More than 40 MIO articles have been published in the last 15 years, but most studies had small numbers, lacked control groups, and used limited outcome measures. Studies generally showed that MIO was feasible in dogs and cats with low complication rates. The current evidence does not demonstrate superior bone healing or functional outcomes with MIO when compared to standard methods. Although treatment principles, case selection, and techniques varied depending on the anatomical location, there were no salient differences in complication rates among long bones, physeal, and articular fractures treated by MIO. Conclusion: The current available evidence and the personal experience of the authors support MIO as a promising fracture management modality. MIO can yield excellent outcomes when applied in carefully selected cases, performed by surgeons experienced in the technique. We cannot, however, conclude that MIO is superior to open fracture stabilization based on the available evidence in veterinary literature. Randomized controlled studies are warranted to prospectively compare MIO with other osteosynthesis techniques and thereby validate its role in fracture management for dogs and cats

Peripheral pulmonary artery aneurysm presenting as a solitary pulmonary nodule

We report the case of a 63-year-old female patient who was evaluated due to a solitary pulmonary nodule. The final diagnosis was a solitary peripheral pulmonary artery saccular aneurysm. The patient was submitted to a pulmonary lobectomy with excellent recovery. Peripheral pulmonary artery aneurysms that arise from segmental or intrapulmonary branches are extremely rare, and their management is still controversial

Automatic design of interpretable control laws through parametrized Genetic Programming with adjoint state method gradient evaluation

Marchetti, F., Pietropolli, G., Verdù, F. J. C., Castelli, M., & Minisci, E. (2024). Automatic design of interpretable control laws through parametrized Genetic Programming with adjoint state method gradient evaluation. Applied Soft Computing, 159, 1-17. Article 111654. https://doi.org/10.1016/j.asoc.2024.111654 --- This work was supported by national funds through the FCT (Fundação para a Ciência e a Tecnologia) by the project UIDB/04152/2020-Centro de Investigação em Gestão de Informação – MagIC/NOVA IMS; and by the SPECIES Society through the SPECIES Scholarship 2022.This work investigates the application of a Local Search (LS) enhanced Genetic Programming (GP) algorithm to the control scheme’s design task. The combination of LS and GP aims to produce an interpretable control law as similar as possible to the optimal control scheme reference. Inclusive Genetic Programming (IGP), a GP heuristic capable of promoting and maintaining the population diversity, is chosen as the GP algorithm since it proved successful on the considered task. IGP is enhanced with the Operators Gradient Descent (OPGD) approach, which consists of embedding learnable parameters into the GP individuals. These parameters are optimized during and after the evolutionary process. Moreover, the OPGD approach is combined with the adjoint state method to evaluate the gradient of the objective function. The original OPGD was formulated by relying on the backpropagation technique for the gradient’s evaluation, which is impractical in an optimization problem involving a dynamical system because of scalability and numerical errors. On the other hand, the adjoint method allows for overcoming this issue. Two experiments are formulated to test the proposed approach, named Operator Gradient Descent - Inclusive Genetic Programming (OPGD-IGP): the design of a Proportional-Derivative (PD) control law for a harmonic oscillator and the design of a Linear Quadratic Regulator (LQR) control law for an inverted pendulum on a cart. OPGD-IGP proved successful in both experiments, being capable of autonomously designing an interpretable control law similar to the optimal ones, both in terms of shape and control gains.publishersversionpublishe