Publications

Background context Spinopelvic alignment assessment needs to account for pelvic incidence (PI). Purpose This study aimed at providing normative values for commonly used parameters in whole-body alignment analysis based on PI. Design Multicentric prospective study. Patient sample This study included healthy volunteers with full-body biplanar radiograph in free-standing position. Outcome measures All radiographic data were collected from 3D reconstructions: Sagittal vertical axis (SVA), T1 pelvic angle (TPA), spino-sacral angle (SSA), sagittal odontoid-hip axis angle (ODHA), pelvic parameters, sacro-femoral angle (SFA), knee flexion angle (KFA), ankle flexion angle (AA), Pelvic shift (PSh), lumbar lordosis (LL), thoracic kyphosis (TK) and cervical lordosis (CL). Methods Population was divided into five groups according to PI. Normative values were described for each group. Linear regressions including age and PI provided prediction formulas for PT, TPA, SSA and SFA. Results 642 subjects were included. Mean age was 37.7 ± 16.3 years (range: 18–90). Mean PI in the cohort was 49.3 ± 9.5°. LL, PT, SFA, SSA and TPA correlated with PI and age. ODHA, TK, CL and the other lower limb parameters were not associated with PI. All normative values across PI groups are provided for segmental, regional and global alignment parameters. Prediction formulas were: PT=-12.7 + 0.38*PI + 0.14*Age, TPA=-16.9 + 0.34*PI + 0.15*Age, SSA = 109.8 + 0.58*PI-0.19*Age, and SFA = 173 + 0.39*PI + 0.11*Age. Conclusions SSA, PT, TPA and SFA must be assessed according to patient’s PI. This study provides normative values for each PI group, and predictive formulas taking age and PI into account. PI cannot be used to define thoracic and cervical curvatures.

Accurate estimation of joint load during a lifting/lowering task could provide a better understanding of the pathogenesis and development of musculoskeletal disorders. In particular, the values of the net force and moment at the L5-S1 joint could be an important criterion to identify the unsafe lifting/lowering tasks. In this study, the joint load at L5-S1 was estimated from the motion kinematics acquired using a multi-view markerless motion capture system without force plate. The 3D human pose estimation was first obtained on each frame using deep learning. The kinematic analysis was then performed to calculate the velocity and acceleration information of each segment. Then, the net force and moment at the L5-S1 joint were calculated using inverse dynamics with a top-down approach. This estimate was compared to a reference with a bottom-up approach. It was computed using a marker-based motion capture system combined with force plates and using personalized body segment inertial parameters derived from a 3D model of the human body shape constructed for each subject using biplanar radiographs. The average differences of the estimates for force and moment among all subjects were 14.0 ± 6.9 N and 9.0 ± 2.3 Nm, respectively. Meanwhile, the mean peak value differences of the estimates were 10.8 ± 8.9 N and 11.9 ± 9.5 Nm, respectively. This study then proposed the most rigorous comparison of mechanical loading on the lumbar spine using computer vision. Further work is needed to perform such an estimation under realistic industrial conditions.

This study investigates fuzzy fiber composites, characterized by a viscoplastic matrix and fuzzy fibers, i.e. fibers coated with radially aligned carbon nanotubes (CNTs). A comprehensive micromechanical framework is developed to model and optimize these composites, with a particular emphasis on interfacial damage mechanisms introduced through microvoids growth in the region between the fuzzy fibers and the matrix. By developing an equivalent fiber model, the complexity of the multi-phase structure is effectively reduced, facilitating efficient parametric analyses. Various homogenization techniques, including Composite Cylinder Assemblage (CCA), Transformation Field Analysis (TFA), and periodic homogenization, are combined to predict the overall stress-strain responses of the equivalent fiber approach and then the full fuzzy fiber composite. The identification of the framework and model parameters enabled a parametric/sensitivity analysis to study the effect of varying key parameters, including the volume fraction. The results of this paper contribute to a deeper understanding of unidirectional fuzzy fiber composites and establish a foundation for future parametric investigations and fuzzy fiber composite applications accounting for nonlinear regimes.

Urban overheating poses significant challenges to public comfort and health, particularly in pedestrian areas. While urban climate studies offer detailed maps of thermal discomfort and heat stress, urban planning often relies on simplified guidelines, creating a gap between research and practice. This study introduces a methodology to bridge this gap by developing a spatially aggregated dissatisfaction indicator, PPD*^, based on the Universal Thermal Climate Index (UTCI) and incorporating a minimum spatial requirement for shade derived from existing cities' shading policies. The novel indicator separately accounts for thermal discomfort in both shaded and sunlit pedestrian areas. A simulated case study in a neighborhood in La Rochelle, France, evaluates six tree planting scenarios, with canopy cover ranging from 0% to 80%. Results indicate that a 20% canopy cover is a practical threshold for mitigating discomfort in moderate and warm climates. This methodology can also be extended to assess additional cooling strategies, such as evaporative systems, and provides valuable insights for optimizing cost-effective and sustainable urban adaptation measures. © 2025

Failure monitoring of industrial systems is imperative in order to ensure their reliability and competitiveness. This paper presents an innovative hybrid modelling approach applied to DC electric motors, specifically the Kollmorgen AKM42 servomotor. The proposed Cooperative Hybrid Model for Classification (CHMC) combines physics-based and data-driven models to improve fault detection and extrapolation to new usage profiles. The integration of physical knowledge of the healthy behaviour of the motor into a recurrent neural network enhances the accuracy of bearing fault detection by identifying three health states: healthy, progressive fault and stabilised fault. Additionally, Singular Value Decomposition (SVD) is employed for the purposes of feature extraction and dimensionality reduction, thereby enhancing the model’s capacity to generalise with limited training data. The findings of this study demonstrate that a reduction in the input data of 90% preserves the essential information, with an analysis of the first harmonics revealing a narrow frequency range. This elucidates the reason why the first 20 components are sufficient to explain the data variability. The findings reveal that, for usage profiles analogous to the training data, both the CHMC and NHMC models demonstrate comparable performance without reduction. However, the CHMC model exhibits superior performance in detecting true negatives (90% vs. 89%) and differentiating between healthy and failure states. The NHMC model encounters greater difficulty in distinguishing failure states (83.92% vs. 86.56% for progressive failure). When exposed to new usage profiles with increased frequency and amplitude, the CHMC model adapts better, showing superior performance in detecting true positives and handling new data, highlighting its superior extrapolation capabilities. The integration of SVD further reduces input data complexity, and the CHMC model consistently outperforms the NHMC model in these reduced data scenarios, demonstrating the efficacy of combining physical models and dimensionality reduction in enhancing the model’s generalisation, fault detection, and adaptability. This approach has the advantage of reducing the need for retraining, which makes the CHMC model a cost-effective solution for motor fault classification in industrial settings. In conclusion, the CHMC model offers a generalisable method with significant advantages in fault detection, model adaptation, and predictive maintenance performance across varying usage profiles and on unseen operational scenarios. © 2025 by the authors.

This study presents a detailed comparative analysis between experimental leakage flow rates and numerical predictions for carbon brush seals with long bristles, utilizing a porous medium model approach. A series of tests were carried out on a static rig (without rotor rotation). The experimental setup allows tests under various interference conditions, revealing significant insights into the flow behavior through the brush seal. A numerical model based on the Darcy-Forchheimer equation is developed to interpret the complex flow dynamics within the brush seal, accounting for viscous, compressible, and inertial effects. The study evaluates the impact of brush deformation and porosity on flow resistance, leveraging experimental data to refine the numerical model parameters. This investigation not only deepens the understanding of brush seal flow physics but also improves the predictive accuracy of the numerical model in simulating operational conditions. © 2025 American Society of Mechanical Engineers (ASME). All rights reserved.

The thermal conductivity of Functionally Graded Materials (FGMs) can be efficiently designed through topology optimization to obtain thermal meta-structures that actively steer the heat flow. Compared to conventional analytical design methods, topology optimization allows handling arbitrary geometries, boundary conditions and design requirements and producing alternate designs for non-unique problems. Additionally, as far as the design of meta-structures is concerned, topology optimization does not need intuition-based coordinate transformation or the form invariance of governing equations, as in the case of transformation thermotics. We explore isogeometric density-based topology optimization in the continuous setting, which perfectly aligns with FGMs. In this formulation, the density field, geometry and solution of the governing equations are parameterized using non-uniform rational basis spline entities. Accordingly, the heat conduction problem is solved using Isogeometric Analysis. We design various 2D & 3D thermal meta-structures under different design scenarios to showcase the effectiveness and versatility of our approach. We also design thermal meta-structures based on architected cellular materials, a special class of FGMs, using their empirical material laws calculated via numerical homogenization.

This critical review examines advances in preprocessing and remelting processes for aluminium alloy chip recycling, emphasizing pre-treatment and remelting techniques that improve both resource recovery and material quality. Pre-treatment strategies, particularly cleaning methods and compaction are critically evaluated. Various cleaning methods, including centrifugation, ultrasonic solvent washing, extraction, and distillation are compared based on their ability to remove residual cutting fluids. Cold compaction, which augments chip density to approximately 2.5 g/cm³, significantly curtails oxidation losses and enhances metal recovery. During remelting, NaCl-KCl-based fluxes with limited fluoride additions (e.g., 3–7 wt% Na₃AlF₆) disrupt oxide networks but require careful dosage control to minimize furnace corrosion and environmental hazards. Moreover, mechanical stirring combined with suitable melting temperatures reduces porosity while enhancing melt purity. Future research should prioritize the development of low-energy cleaning methods, flux composition optimization, and scalable production techniques to further advance sustainable aluminium recycling.

Knowing the thermo-mechanical history of a part during its processing is essential to master the final properties of the product. During forming processes, several parameters can affect it. The development of a surrogate model makes it possible to access history in real time without having to resort to a numerical simulation. We restrict ourselves in this study to the cooling phase of the casting process. The thermal problem has been formulated taking into account the metal as well as the mould. Physical constants such as latent heat, conductivities and heat transfer coefficients has been kept constant. The problem has been parametrized by the coolant temperatures in five different cooling channels. To establish the offline model, multiple simulations are performed based on well-chosen combinations of parameters. The space-time solution of the thermal problem has been solved parametrically. In this work we propose a strategy based on the solution decomposition in space, time, and parameter modes. By applying a machine learning strategy, one should be able to produce modes of the parametric space for new sets of parameters. The machine learning strategy uses either random forest or polynomial fitting regressors. The reconstruction of the thermal solution can then be done using those modes obtained from the parametric space, with the same spatial and temporal basis previously established. This rationale is further extended to establish a model for the ignored part of the physics, in order to describe experimental measures. We present a strategy that makes it possible to calculate this ignorance using the same spatio-temporal basis obtained during the implementation of the numerical model, enabling the efficient construction of processing hybrid twins.

This study investigates the effectiveness of Immersive Virtual Reality (IVR) compared to traditional slideshow lessons in teaching implicit knowledge. For this purpose, the research focuses on professional decision-making skills in viticulture. Most existing research on immersive learning concentrates on explicit learning strategies. In contrast, this study explores the potential of IVR to foster the transfer of implicit knowledge to real-world situations.Forty third-year engineering students were randomly assigned to an IVR or a traditional slideshow group. They learned to assess vine vigour through an implicit learning phase, followed by a real-world evaluation in an actual vineyard. Learning outcomes were measured by decision-making accuracy, response time, and intrinsic motivation.The findings show that the IVR group did not significantly outperform the slideshow group in decision-making accuracy. However, the IVR group took more time to make decisions. This observation suggests an impact of immersion during the transfer to real-world situations. Additionally, the IVR group showed a higher level of intrinsic motivation than the slideshow group.These results suggest that although the immersion effect does not directly enhance learning outcomes for this cognitive objective, it does affect how knowledge is transferred to the real world. They also confirm that the positive impact of immersion is difficult to generalize and may depend on the nature of the knowledge. Still, the immersion effect significantly improves learner motivation. This consistent finding could be a key factor in long-term educational success. Further research exploring the nuanced effects of immersion on different learning strategies and educational objectives could offer new practical perspectives for the future of educational technologies.