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A review on deep learning for vision-based hand detection, hand segmentation and hand gesture recognition in human–robot interaction
No full textHand-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
An exploratory study of workplace Occupational Safety and Health Interventions and their impact on productivity and performance in an Ageing Workforce
No full textDuring the last decades, a significant change has taken place in the demographic characteristics of the workforce: a growing percentage of employees is ageing, and organizations are beginning to feel the impact of an Ageing Workforce (AW) on production system performance. Yet this aspect can play a significant role in effective Occupational Safety and Health (OSH) decision-making, it is often underestimated or overlooked. Hence, an exploratory questionnaire-based study—grounded in the existing literature—maps common OSH interventions in an AW context and assesses their impact on OSH performance and productivity. Additionally, the study investigates key drivers and barriers—both ageing-specific and more general—that shape, facilitate, or limit intervention outcomes. Findings reveal that targeted communication and training interventions, along with ergonomic and technical tools, most strongly enhance OSH performance and productivity for AWs. Key drivers include active worker involvement and management commitment, whereas barriers such as poor safety culture, limited risk awareness, and legislative complexity hinder success. Overall, while economic incentives and policy compliance matter, they are not sufficient to drive sustainable improvements in OSH performance. Instead, internal organizational dynamics—especially cultural and relational factors that emphasize stability, recognition, and meaningful participation—are the ones that most influence the successful deployment of effective, inclusive interventions in an AW context
Creep Deformation Estimation of Single Crystal Ni-Based Superalloy by Optimized Geometrically Necessary Dislocation Density Evaluation
No full textIn the framework of high temperature components, the need to evaluate the accumulated creep damage during service life is fundamental to extend the life of components which are currently deemed as scrap as per design intent. Thus, the life assessment of Ni-based superalloys could be performed in relation to the accumulated creep deformation which represents the limiting factor for serviced components. Despite the different microstructural changes that occur in service life, this work focuses on the possibility to evaluate the material strain by means of electron backscattered diffraction (EBSD). The key point is the identification of the correlation between geometrically necessary dislocation (GND) density derived from EBSD analyses and the reached creep strain for a single crystal Ni-based superalloy. However, the results of GND density are affected by the settings’ parameters adopted to perform the analysis by the magnification level and the step size. These two parameters have been optimized by analyzing specimens from interrupted creep tests at strain levels between 0.5% and 10%, in the temperature range between 850 °C and 1000 °C
On a semilinear parabolic equation with time-dependent source term on infinite graphs
No full textWe are concerned with semilinear parabolic equations, with a time-dependent source term of the form h(t)uq with q>1, posed on an infinite graph. We assume that the bottom of the L2-spectrum of the Laplacian on the graph, denoted by λ1(G), is positive. In dependence of q, h(t) and λ1(G), we show global in time existence or finite time blow-up of solutions
Coordinated Manipulator/Spacecraft Control with Systematic Gain Tuning for Space Robot Operations
No full textThis paper presents a modeling and control framework tailored for spacecraft equipped with robotic manipulators, focusing on capture maneuvers and stabilization of uncontrolled target objects. The proposed control strategy combines feedback linearization and a quasi-time-optimal feedback law for the precapture phase and a Lyapunov-based design for postcapture stabilization, with emphasis on addressing actuator saturation. Control gains are tuned using a H infinity synthesis approach, accommodating various dynamics, including sloshing and actuator dynamics effects. Simulation results demonstrate the effectiveness of the proposed designs in two representative scenarios: servicing a large geostationary platform and a small satellite within a low-Earth-orbit constellation
Sustainability Assessment of Circular Technologies in Agriculture: Overview of Evaluation Methodologies and Research Challenges
No full textGlobal demand for food is expected to grow significantly by 2050, underlying the urgency of a sustainable transition in agriculture. In this context, the Circular Economy (CE) paradigm emerges as a promising strategy. This transition is still ongoing, underscoring the importance of sustainability assessment as the first crucial step in supporting this process effectively. Therefore, comprehensive and robust evaluation tools and methodologies are necessary to support effective decision-making processes in this context. This study addresses this topic by conducting a literature review focused on the main evaluation methodologies adopted to assess the sustainability of circular technologies in agriculture, as well as to identify emerging research trends and to identify current knowledge gaps. Therefore, the main objective of this research is to establish a well-defined framework that starting from existing researches, it will support the development of future research directions. The performed review identifies Life Cycle Assessment (LCA) as the most applied methodology for environmental impact assessment, due to its ability to analyze environmental impacts and resources consumption throughout the entire life-cycle of a product, followed by Multi-Criteria Analysis (MCA) and performances-based models for their capacity of integrating and managing many dimensions (environmental, economic, and social) within the evaluation process. Emerging trends highlight the increasing adoption of computational approaches, such as System Dynamics (SD), facilitating a more comprehensive assessment of complex agricultural systems. Despite this increasing attention, the review addresses the significant gap, or rather, the limited management of stakeholders’ conflicts and synergies. This gap will inform potential research directions within the Agritech project, especially regarding the development of Social Multi-Criteria Evaluation (SMCE) to integrate stakeholders’ perspectives in the sustainability assessment of circular technologies
VISTA: Dust and Volatile Sensor for the ESA Hera Mission
No full textVISTA (Volatile In-Situ Thermogravimeter Analyzer) is a QCM (Quartz Crystal Microbalance) based device designed to characterize the dust environment of the Didymos asteroid system by detecting the presence of dust particles with sizes smaller than 5-10 μm and sub-μm, as well as volatiles and light organics, within the framework of the European Space Agency (ESA) Hera Mission. Thanks to its customized subsystems design, VISTA is capable of monitoring deposition and desorption/sublimation processes in the space environment, as well as molecular contamination (in support to other instruments) originating from outgassing sources during different mission phases, and of performing Thermo-Gravimetric Analysis (TGA) on the collected materials. The VISTA Model development for Hera started in 2020 and continued through 2023, and included an Engineering Qualification Model 0 (EM0), an Engineering Qualification Model (EQM), a Flight Model (FM) and a Flight Spare (FS). The EM0 was electrically and mechanically representative of VISTA, while the EQM, FM and FS share the same mechanical structure and electrical connections. The prototype, as well as the Engineering, Flight and Spare Models, were delivered to the prime contractor, Tyvak International. VISTA Models are composed by three different subsystems: two quartz crystals mounted in a sandwich-like configuration; the Proximity Electronics; and the Thermal Control System, which includes two integrated and customized heaters and a Thermo-Electric Cooler to cool the sensor and facilitate dust and volatiles deposition. The VISTA EQM, FM and FS successfully passed the Qualification and Acceptance Test Campaigns. The main results obtained from simulations of particles and volatiles deposition in a vacuum chamber, heating cycles, and Thermo-Gravimetric Analyses are reported in this work
In Situ Characterization of Strontium Titanium Ferrite Perovskites for Application as Electrodes of Solid Oxide Cells
No full textThis study investigates the redox behavior, the surface composition, and the electrochemical performance of the Ni-doped Sr0.95(Ti0.3Fe0.63Ni0.07)O3 (STF-Ni) and SrTi0.3Fe0.7O3 (STF) perovskites under conditions relevant to solid oxide cell applications. The effect of Ni doping on exsolution and on its reversibility is examined with synergistic characterization techniques. In situ XRD experiments (in 5% H2, up to 750 °C) reveal that FeNi and FeNi3 coexist in alloyed Ni–Fe nanoparticles and that the exsolved Ni is only partially reincorporated in the lattice of STF-Ni on reoxidation in air. In contrast, the reduction of STF leads to the segregation of nonalloyed metallic Fe particles. In situ near-ambient pressure XPS experiments (20 mbar, 550 °C) show that Sr segregates on both perovskites as SrOx during reduction in pure H2, and that the exsolution of Ni and Fe enhances the segregation. Subsequent exposure to CO2 causes the formation of SrCO3 and compositional changes of the STF-Ni nanoparticles, which become richer in Ni due to the back-diffusion of Fe in the lattice. When applied as fuel electrodes of electrolyte-supported solid oxide cells, STF-Ni and STF exhibit distinct behaviors in H2 electro-oxidation and CO2 electrolysis. STF-Ni shows better performance than STF with 3% humidified H2 supply (450 vs 350 mW/cm2 at 0.5 V), while both electrodes achieve similar current density (−450 mA/cm2 at 1.4 V) in reversible CO2 electrolysis with a 50/50 CO/CO2 mixture. These performance differences primarily arise from the interaction with CO2, which causes SrCO3 formation, electrode passivation, and compositional modifications of the nanoparticles