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Evaluating mental workload: A taxonomic approach to evaluation tools based on ISO 10075
Full text linkRecent technological transitions present in the manufacturing sector are transforming the way operators work,
greatly impacting their mental workload and presenting new challenges for the cognitive ergonomics field. In an
effort to design and implement human-centered management strategies, it is increasingly necessary to effectively
explore and evaluate the various aspects related to mental workload. Despite the maturity of the field, in the
existing literature a consensus on defining mental workload remains elusive. To bridge this gap, this paper in troduces a comprehensive and holistic definition, drawing inspiration from the ISO 10075 standard. This novel
taxonomic framework distinguishes various constructs related to mental workload, clarifying their causes and
the consequences they induce. Additionally, an extensive literature analysis was conducted to develop a tax onomy of mental workload assessment tools, outlining their operational principles along with their strengths and
weaknesses. This taxonomy can be used as a pragmatic guide for selecting appropriate tools based on specific
contexts. Moreover, the existing literature was extensively investigated to ascertain the nature of the correla tion—whether positive or negative—between the metrics of these tools and the range of the effects of mental
workload fluctuations. This methodological approach addresses the common issue of misinterpreting results from
mental workload measurements.
In essence, the research seeks to enrich the understanding of mental workload concepts and offers a critical
evaluation of its measurement tools. The findings are intended to assist practitioners in the informed selection of
measurement tools and to refine data interpretation, thereby facilitating better management of mental workload
in manufacturing environments
Ultrasound induced hydrogen release from ammonia borane mediated by oxidized multiwalled carbon nanotube under hydrolytic conditions
Full text linkThe field of hydrogen storage is one of the last frontiers in the exploitation of hydrogen-based technology. Particularly, the utilization of ammonia borane is a very promising route to solve the issue related to hydrogen storage due to the content of hydrogen up to 19.8 wt% and the stability in the ambient temperature and pressure conditions. Nevertheless, the hydrogen release from ammonia borane is quite complex under thermal stimuli, with several secondary compounds released. Alternatively, hydrolysis of ammonia borane is a simpler route to release of hydrogen in presence of water without any side reaction when a catalyst is used. This study investigates the ultrasound-assisted hydrolytic dehydrogenation of ammonia borane mediated by oxidized multiwalled carbon nanotubes (MWCNTs) as a metal-free energy-efficient catalytic system. The application of ultrasonic irradiation significantly enhanced the catalytic performance by promoting mass transport, improving water molecule activation, and increasing the dispersion and reactivity of the oxidized MWCNTs in the water medium. The oxidized MWCNTs promote the activation of ammonia borane, reducing the activation energy of the systems over 77% and reaching a remarkable hydrogen release efficiency with a conversion of up to 98%
Assessment of a Thrust Measurement System for Supersonic Nozzles
No full textThis paper presents a measurement system to assess the thrust produced by advanced aerospace propulsion nozzles. The system is able to evaluate all thrust components in the case of
vectorized nozzles using three precision scales positioned at the base of the test rig. The scales are arranged in a 120-degree configuration to accurately reconstruct the thrust vector. A calibration of the thrust measurement system was carried out. Cold flow experiments using air as working fluid will be performed for a linear aerospike nozzle in di!erential throttling configurations.
The measured forces are compared with those resulting from numerical simulations conducted
on a nozzle model with the same geometry
Physics-Based Region Clustering to Boost Inference on Computational Fluid Dynamics Flow Fields
No full textThe high dimensionality and variability of Computational Fluid Dynamics (CFD) data pose a significant challenge for Machine Learning (ML) models. The only solutions in the literature addressing inference from CFD flow fields are based on expert-driven features, which consist of fluid dynamic quantities averaged on specific regions of the entire computational domain. However, using handcrafted features can limit the scalability and portability of existing methods, and result in the loss of critical flow field information that might be essential for capturing non-linear patterns inherent in the CFD data. We propose a method to replace handcrafted features with features defined on regions obtained by clustering. Our approach combines: i) physics-based clustering, to identify meaningful regions within the flow field, ii) cluster-based feature extraction, to capture localized fluid dynamics properties, and iii) set-learning models to process the extracted information. Our solution allows integrating physics-based modeling with ML, and provides a portable and flexible pipeline capable of effectively dealing with the variability and dimensionality of CFD flow fields. We validate our method on publicly available CFD datasets (from the aerospace domain) and apply it to a realistic scenario, that is, the classification of pathologies in real 3D human upper airways extracted from CT scans, acquired in collaboration with a medical hospital. Experimental results demonstrate the accuracy and scalability of our method, and highlight its potential for leveraging CFD data in ML frameworks for other scientific and engineering applications
In-situ UV cured deep eutectic solvent-based gel polymer electrolyte for Li metal batteries
No full textLithium metal batteries (LMBs) are a promising alternative to obtain high-performance and sustainable energy storage systems, overcoming the energy density limitations of current lithium-ion batteries. However, Li metal hinders their widespread implementation due to high chemical reactivity and dendritic growth, poor interfacial stability and safety concerns linked to the use of flammable liquid electrolytes. Gel polymer electrolytes (GPEs), incorporating non-flammable solvents, have emerged as viable solutions, combining mechanical stability with good electrochemical performance. In this work, a scalable and cheap in-situ fabrication process for a deep eutectic solvent (DES)-based GPE is presented, directly depositing and UV-crosslinking the precursor formulation on the cathode surface. The polyethylene glycol diacrylate-based polymeric matrix is cured in a lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) and trifluoroacetamide (TFA)-based DES, with and without the addition of ethylene carbonate and diethyl carbonate mixture (g-DES and g-DES10, respectively). Physico-chemical and electrochemical characterizations reveal weaker Li+ coordination and improved transport properties and favourable interfacial properties in the presence of the carbonates. Li||LFP cells sporting g-DES10 GPEs exhibited good interfacial stability and stable cycling (163 mAh g−1 over 113 cycles) at room temperature and 1C, demonstrating the suitability of such production processes for scalable, safe and high-performance LMBs
Poroelasticity derived from the microstructure for intrinsically incompressible constituents
No full textWe provide a new derivation of the quasi-static equations of Biot’s poroelasticity from the microstructure via the asymptotic (periodic) homogenisation method (AHM) by assuming intrinsic incompressibility of both an isotropic, linear elastic solid and a low Reynolds’ number Newtonian fluid, in a small deformations regime. This is done by starting from a fluid–structure interaction (FSI) problem between the two phases at the pore scale, and by introducing both a solid and a fluid pressure, as both phases are equipped with an incompressibility constraint. Upscaling by the AHM then results in the expected Biot’s equation at the macroscopic scale, with coefficients which are to be computed by solving non-standard periodic cell problems at the pore scale. These latter differ from the ones arising from classical derivations of poroelasticity via the AHM, which are typically obtained by assuming that the elastic phase is compressible, and are characterised by a saddle point structure which is inherited from the equations governing the original FSI problem. The proposed approach, which is new and cannot be derived as a particular case of existing formulations, means that the poroelastic governing equations for intrinsic incompressible phases are obtained without performing any “a posteriori” assumption on the macroscale coefficients, as these latter are typically employed based on physical arguments rather than following from a rigorous analysis of the properties of the pore scale cell problems. The advantages of the current formulation for incompressible solids are as follows. (a) The formulation is derived for two genuinely incompressible phases and in particular for an incompressible solid, which means that a reduced number of input parameters is required to compute the effective stiffness. In the case of pore scale isotropy, this means that only the shear modulus is to be provided. (b) The pore scale cell problems can be solved without approximating the pore scale elastic properties to that of an incompressible solid, i.e. in the case of isotropy, no additional errors are to be introduced by utilising approximate values of the Poisson’s ratio (which in a compressible formulation can be close to, but not identical to, 0.5)
Knowledge spillovers from green technologies
No full textThis paper analyses and compares the morphology of knowledge spillover networks based on green versus non-green technologies to assess whether one of the two domains diffuses more widely, travels farther in the geographical space, and connects more extensively otherwise distant technological fields. We employ various metrics based on forward citations to measure knowledge spillovers from patented technologies. Our sample includes bibliometric data of 3,809,214 patent applications filed at the European Patent Office from 1981 to 2020, of which 316,897 (8.32%) are classified as green. Results show that green patents receive more citations – both directly and indirectly – from follow-on inventions than non-green ones, thus contributing to a greater knowledge diffusion throughout the entire spillover network. We also find that the spectrum of sectors to which a green knowledge spillover network is targeted tends to be broader. Lastly, the inherent nature of green technologies, along with their potential to act as boundary spanners, results in knowledge transmission within the green do-main being less geographically localized than for non-green technologies
A novel state estimation framework for a four-wheeled lunar rover with active articulated suspensions
Full text linkIn recent years, space agencies and private companies have shown a renewed interest in Moon exploration, with the ultimate goal of having a permanent human presence on the surface by the 2030s. A crucial step in this direction involves deploying robotic missions. To this purpose, at Thales Alenia Space Italia S.p.A., a versatile, multipurpose four-wheeled robotic platform with active suspensions has been designed as a common reference mobility system able to perform a multitude of tasks on the Moon, spanning from South Pole exploration to In Situ Resource Utilization. In this context, we propose a state estimation framework based on factor graph optimization to perform sensor fusion and estimation of rover odometry. The implementation relies on the ROS 2 package Fuse, which has been optimized and extended with 3D sensors and motion models. The paper contributions are twofold: firstly, we developed a method for estimating the rover s linear and angular body velocities based on data from the wheel-steer-suspension assembly encoders. Three-dimensional components of the body velocity and the associated covariance matrix are computed by accurately determining the plane of instantaneous motion of the rover. Secondly, dedicated sensor models are used to fuse the estimated body velocities with readings from the on-board IMU and with odometry input computed from a visual pipeline. The latter can be obtained both from a stereo camera by matching visual features, or by registering point clouds gathered by time-of-flight sensors, allowing autonomous navigation in any lighting condition. Constraints derived from different sensors are joined by leveraging a motion model that encapsulates the entire span of locomotion modalities allowed by the rover geometry. The method has been validated in simulations built on Project Chrono and with the rover prototype navigating in a representative facility. Results demonstrate that the framework is highly optimized, efficiently facilitating the integration of multiple sensor readings from various sources, delivering fused odometry outputs at a high frequency, and ensuring accurate and real-time state updates
Rethinking Education on Critical Infrastructure Resilience and Risk Management: Insights from a Systematic Review
Full text linkThe growing complexity and interdependence of critical infrastructures (CIs), increasingly exposed to natural and technological hazards, call for educational approaches to enhance resilience and risk management. This study examines trends, patterns, and challenges in integrating digital and immersive technologies into education and training for stakeholders in critical infrastructure management. A systematic review of peer-reviewed literature was conducted using Scopus as the primary source, covering the last decade and analyzing the corpus across six dimensions: technological approach, pedagogical model, hazard typology, infrastructure domain, stakeholder category, and implementation phase. Following the PRISMA framework, 5635 records were identified and screened through a multistage process combining rule-based filtering and manual review, resulting in 105 papers meeting the inclusion criteria. The analysis reveals a shift from classroom instruction and physical drills toward immersive, simulation-based, and data-informed learning ecosystems that strengthen situational awareness, procedural accuracy, and decision-making under stress. However, the review identifies persistent gaps in evaluation metrics, cross-sector frameworks, and collaborative learning environments that limit adoption. The findings underscore that digital and immersive technologies can reconfigure education and training frameworks, enabling the formation of Resilient Operators endowed with adaptive cognition, continuous learning capacities, and responsiveness to natural hazard-induced technological risks