Breast cancer is the most frequently diagnosed tumor pathology on a global scale, being the leading cause of mortality in women. In light of this problem, screening programs have been implemented on the population at risk in the form of mammograms, starting in the 20th century. This has considerably reduced the associated deaths, as well as improved the prognosis of the patients who suffer from this disease. In spite of this, the evaluation of mammograms is not without certain variability and depends, to a large extent, on the experience and training of the medical team carrying out the assessment. With the aim of supporting the evaluation process of mammogram images and improving the diagnosis process, this work presents the design, development and proof of concept of a novel intelligent clinical decision support system, grounded on two predictive approaches that work concurrently. The first of them applies a series of expert systems based on fuzzy inferential engines, geared towards the treatment of the characteristics associated with the main findings present in mammograms. This allows the determination of a series of risk indicators, the Symbolic Risks, related to the risk of developing breast cancer according to the different findings. The second one implements a classification machine learning algorithm, which using data related to mammography findings as well as general patient information determines another metric, the Statistical Risk, also linked to the risk of developing breast cancer. These risk indicators are then combined, resulting in a new indicator, the Global Risk. This could then be corrected using a weighting factor according to the BI-RADS category, allocated to each patient by the medical team in charge. Thus, the Corrected Global Risk is obtained, which after interpretation can be used to establish the patient’s status as well as generate personalized recommendations. The proof of concept and software implementation of the system were carried out using a data set with 130 patients from a database from the School of Medicine and Public Health of the University of Wisconsin-Madison. The results obtained were encouraging, highlighting the potential use of the application, albeit pending intensive clinical validation in real environments. Moreover, its possible integration in hospital computer systems is expected to improve diagnostic processes as well as patient prognosis.

The Furuta pendulum is a well-known benchmark in the field of underactuated mechanical systems due to its reduced number of control inputs compared to its degrees of freedom, and richly nonlinear behavior. This work addresses the challenge of accurately modeling and controlling such a system without relying on traditional linearization techniques. In contrast to the common approach based on Lagrangian analytical modeling and state–space linearization, we propose a methodology that integrates a high-fidelity multibody model developed in Simscape Multibody (MATLAB), capturing the complete nonlinear dynamics of the system. The multibody model includes all geometric, inertial, and joint parameters of the physical hardware and interfaces directly with Simulink, enabling realistic simulation and control integration. To validate the physical fidelity of the multibody model, we perform a frequency-domain analysis of the pendulum’s natural free response. The dominant vibration frequency extracted from the simulation is compared with the theoretical prediction, demonstrating accurate capture of the system’s inertial and dynamic properties. This validation strategy strengthens the reliability of the model as a digital twin. The classical analytical formulation is provided to validate the simulation model and serve as a comparative framework. This dual modeling strategy allows for benchmarking control strategies against a trustworthy nonlinear digital twin of the Furuta pendulum. Preliminary experimental results using a physical prototype validate the feasibility of the proposed approach and set the foundation for future work in advanced nonlinear control design using the multibody representation as a digital validation tool.

This work investigates multimode vibration suppression in quay-side container cranes by combining analytical modeling, nonlinear multibody simulation, robust Input Shaping, and experimental implementation. A linearized double-pendulum model preserving the main geometric and inertial properties of the spreader–ISO-container assembly is first derived to characterize its coupled dynamics, comprising a low-frequency global pendular mode and a higher-frequency relative ringing mode. The errors introduced by linearization are assessed against a higher-fidelity Simscape Multibody model over a wide range of operating configurations. The comparison shows that the analytical formulation provides sufficiently accurate estimates of the modal frequency ranges for robust shaper design. Two Unity Magnitude One-Hump shapers are therefore synthesized for the identified low- and high-frequency bands and subsequently convolved into a multimode bang–off–bang command. Sensitivity analysis confirms simultaneous attenuation of both target frequency ranges. Numerical validation on the nonlinear multibody model shows that the reduction in residual vibration remains above 90% over the investigated variations in hoisting length and ISO-container center-of-mass position. Finally, a PLC-based laboratory implementation demonstrates that Unity Magnitude shaping can be executed through timed command toggles without online convolution, supporting its practical implementation using conventional industrial control hardware.

The well-regarded feedforward control strategy known as Input Shaping is aimed at improving the dynamic response of flexible mechanical systems by reducing overshoot and residual vibration amplitude. Its validity has been confirmed by numerous studies dealing with linear system dynamics. However, its application in nonlinear systems, particularly those influenced by tractive–elastic rolling contact friction, remains a challenging and less explored open research area. This paper investigates whether Input Shaping, without tractive rolling friction compensation, can effectively mitigate vibrations in a trolley–pipe benchmark transport system. In this system, the pipe is modeled as a rolling disc attached to the trolley by a spring at its center of mass, while the trolley itself is connected to a guiding body frame by an additional spring acting as a proportional control. The natural frequencies of the system are analytically estimated and numerically verified from a corresponding well-suited multibody model. Thus, tailored two-mode shapers are designed based on simultaneous constraints and the convolution sum, respectively. Through multibody simulations, this study evaluates the performance of Input Shaping under tractive–elastic rolling contact friction conditions. The findings highlight both the potential and limitations of this control method in addressing nonlinear mechanical systems influenced by tractive–elastic rolling contact friction.

The well-regarded feedforward control strategy known as Input Shaping is aimed at improving the dynamic response of flexible mechanical systems by reducing overshoot and residual vibration amplitude. Its validity has been confirmed by numerous studies dealing with linear system dynamics. However, its application to nonlinear systems, particularly those influenced by tractive-elastic rolling contact friction, remains a challenging and less explored open research area. This paper investigates whether Input Shaping, without tractive rolling friction compensation, can effectively mitigate vibrations in a trolley-pipe benchmark transport system. In this system, the pipe is modeled as a rolling disk attached to the trolley by a spring at its center of mass, while the trolley itself is connected to a guiding body frame by an additional spring acting as a proportional control. The natural frequencies of the system are analytically estimated and numerically verified from a corresponding well suited multibody model. Thus tailored two-mode shapers are designed based on simultaneous constraints and convolution sum respectively. Through multibody simulations, this study evaluates the performance of Input Shaping under tractive-elastic rolling contact friction conditions. The findings highlight both the potential and limitations of this control method in addressing nonlinear mechanical systems influenced by such tractive-elastic rolling contact friction.