Capacity estimation of Lithium-ion batteries through a Machine Learning approach

Lithium-ion Batteries (LiBs) have become of paramount importance due to their employment in application fields, including renewable energy sources and electric vehicles (EVs), which heavily rely on them. This has spurred research efforts to develop battery models capable of predicting and estimating battery behavior to optimize usage and reduce degradation. To this end, key state parameters, including State Of Charge (SOC) and State of Health (SOH), should be accurately estimated. In the literature, many estimation methods are based on the knowledge of the relationship between Open-Circuit Voltage (OCV) and SOC. The latter can be modeled in different ways, organized into three main approaches: table-based, analytical, and artificial intelligence approaches. Among these, Machine Learning approaches have gained popularity and have shown great promise for this purpose. However, previous studies typically require many OCV-SOC data points or entire fragments of the OCV curve, which makes them unsuitable for EV applications. To address this limitation, the present paper develops and validates an ML algorithm to estimate the battery capacity of a LiB using only two experimental OCV points, accounting for different levels of cycle aging. The results demonstrate that the model, when trained on an accelerated-aged battery, can accurately predict the actual capacity of other batteries with similar characteristics but different aging levels

A review on deep learning for vision-based hand detection, hand segmentation and hand gesture recognition in human–robot interaction

Hand-based analysis, including hand detection, segmentation, and gesture recognition, plays a pivotal role in enabling natural and intuitive human–robot interaction (HRI). Recent advances in vision-based deep learning (DL) have significantly improved robots’ ability to interpret hand cues across diverse settings. However, previous reviews have not addressed all three tasks collectively or focused on recent DL architectures. Filling this gap, we review recent studies at the intersection of DL and hand-based interaction in HRI. We structure the literature around three core tasks, i.e. hand detection, segmentation, and gesture recognition, highlighting DL models, dataset characteristics, evaluation metrics, and key challenges for each. We further examine the application of these models across industrial, assistive, social, aerial, and space robotics domains. We identify the dominant role of Convolutional and Recurrent Neural Networks (CNNs and RNNs), as well as emerging approaches such as attention-based models (Transformers), uncertainty-aware models, Graph Neural Networks (GNNs), and foundation models, i.e. Vision-Language Models (VLMs) and Large Language Models (LLMs). Our analysis reveals gaps, including the scarcity of HRI-specific datasets, underrepresentation of multi-hand and multi-user scenarios, limited use of RGBD and multi-modal inputs, weak cross-dataset generalization, and inconsistent real-time benchmarking. Dynamic and long-range gestures, multi-view setups, and context-aware understanding also remain relatively underexplored. Despite these limitations, promising directions have emerged, such as multi-modal fusion, use of foundation models for intent reasoning, and the development of lightweight architectures for deployment. This review offers a consolidated foundation to support future research on robust and context-aware DL systems for hand-centric HRI

Mitigation of vertical vibrations in coupled meta-rods through internal interactions and boundary constraint

Environmental vibrations caused by seismic waves and traffic loads pose increasing risks in urban areas due to their low attenuation and structural impact. To mitigate the in-plane bulk waves and vertical Raleigh wave component generated by ambient vibrations, we proposed a coupled meta-rod assembled from a local resonance (LR) bar and a negative-stiffness (NS) system. This meta-rod is designed in two forms to redistribute the wave energy, where the NS system is introduced as a boundary constraint. We develop analytical models based on the Bloch conditions and the transfer matrix method to solve and investigate dispersion relationship and transmission abilities. Complementarily, finite elements (FE) models are also established to validate the analytical results and further visualise the interaction between the LR bar and NS system during longitudinal wave propagation. The results indicate that most of long-wavelength waves propagate through the low-stiffness medium within the coupled system. As wave energy is redistributed across frequency, an additional attenuation region emerges beyond the LR bandgap due to the maximum in-phase motion between the LR bar and NS system. This internal interaction gradually dominates the attenuation mechanism as the LR bandgap shifts toward lower frequencies under boundary constraints, providing more effective suppression before the resonance frequency. This study offers a promising strategy for the design of efficient vertical vibration mitigation solutions

Multi-criteria Decision Aiding for Adaptive Reuse of Cultural Heritage: An Application in the City of Naples (Italy)

The paper explores the adaptive reuse paradigm for revitalising unmovable cultural heritage assets. More in detail, this research addresses a real-world evaluation demand concerning the assessment of different adaptive reuse strategies for the requalification of the hospital building in Naples (Italy). According to the complexity and multi-perspective nature of the research topic and the evaluation demand, an integrated and multimethodological evaluation framework has been proposed. Different adaptive reuse strategies have been evaluated by combining the SWOT analysis, the Stakeholder analysis and the PROMETHEE method to address and manage the complexity, multidimensionality and multi-values nature of the evaluation demand. The obtained results underline that the final ranking of the proposed adaptive reuse scenarios is based on a multidimensional and multi-perspective evaluation, supporting thus Decision-Makers in identifying the most suitable scenario for the renovation of the unmovable cultural heritage asset according to the intervention objectives

Fatigue Simulation of RC and R/FRC Wind Turbine Foundations for Lifespan Extension

As wind farms age, owners encounter pivotal decisions about either extending the operational lifespan of their facilities or pursuing complete decommissioning and repowering. Apart from the commercial factors guiding these choices, technical considerations must be assessed to gauge the risks associated with the continued operation of an aging fleet. In the context of onshore wind turbines, reinforced concrete shallow foundations stand as crucial components. Typically designed for a 20-year lifespan, there exists a pressing need to extend the life of foundations installed over 15 years ago. Both foundations and other structural turbine components endure cyclic fatigue loads. The utilization of fibre-reinforced concrete (FRC) presents enhanced competitiveness by reducing the need for traditional reinforcement, expediting construction, and delivering sustainability benefits. Furthermore, incorporating fibres can enhance fatigue behaviour, thereby positively impacting the service life of the foundation. This paper presents a preliminary numerical investigation into the influence of added fibres on the fatigue behaviour of wind tower foundations. The fatigue performance of the FRC foundation is compared with the standard behaviour of a conventional reinforced concrete foundation

Shear Strengthening of RC Beams with U-Wrapped FRCM Composites: State of the Art and Assessment of Available Analytical Models

Shear strengthening of existing reinforced concrete (RC) members with externally bonded (EB) fabric-reinforced cementitious matrix (FRCM) composites represents an attractive solution with respect to alternative strengthening techniques. The EB FRCM could be side-bonded, U-wrapped, or fully wrapped around the beam cross section. Compared with analogous research on EB fiber-reinforced polymers (FRPs), limited work was performed to study the contribution of the EB FRCM to the shear strength of RC beams and was mainly focused on the U-wrapped configuration. Although various analytical models to estimate the EB FRCM shear strength contribution were proposed, their accuracy and the role of different parameters on the results obtained were not thoroughly investigated. In this paper, a state of the art on side-bonded and U-wrapped FRCM shear-strengthened RC beams is provided and discussed to highlight the knowledge gaps and identify the main parameters that control the member shear strength. The accuracy of the available analytical models for the U-wrapped configuration is assessed with respect to a database of experimental FRCM shear-strengthened RC beams collated from the literature. The results obtained point out the key features that the analytical model should have to provide accurate and reliable predictions

Combined Long-Term Collision Avoidance and Stochastic Station-Keeping in Geostationary Earth Orbit

To limit the spread of space debris, space situational awareness (SSA) delineates guidelines to preserve current space assets. Developing effective collision avoidance maneuver (CAM) strategies is emerging as a global top priority among the considered countermeasures to debris-generating events. Despite most encounters happening over very short time frames, some conjunctions occur over a longer time window, such as in geostationary Earth orbit (GEO), where the involved objects may have small relative velocities. Besides, external perturbations, particularly the geopotential, lunisolar, and solar radiation pressure ones, exert forces on the spacecraft, causing it to deviate from its designated slot and potentially endanger neighboring satellites. This issue is compounded when considering state uncertainty. The presented work, therefore, introduces convex optimization approaches for long-term CAM and tailored stochastic station-leeping (SK) policy regarding longitude and latitude in this regime. The formulation enables continuous CAM and chance-constrained SK, ensuring satellite adherence to an assigned GEO slot with a given probability. Two kinds of chance constraints are devised: the first one does not consider the correlation between longitude and latitude, but the latter does